Astronomy & Astrophysics manuscript no. sudare_2015_A_v7 c©ESO 2018June 29, 2018

Supernova rates from the SUDARE VST-Omegacam search. I ?

E. Cappellaro1, M.T. Botticella2, G. Pignata3, 4, L. Grado2, L. Greggio1, L. Limatola2, M. Vaccari5, 6, A. Baruffolo1, S.Benetti1, F. Bufano7, M. Capaccioli8, E. Cascone2, G. Covone8, D. De Cicco8, S. Falocco8, M. Della Valle2, M.

Jarvis9, 5, L. Marchetti10, N. R. Napolitano2, M., Paolillo8, 11, A. Pastorello1, M. Radovich1, P. Schipani2, S. Spiro1, L.Tomasella1, and M. Turatto1

1 INAF, Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, Padova, 35122 Italy2 INAF, Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, Napoli, 80131 Italy3 Departemento de Ciencias Fisicas, Universidad Andres Bello, Santiago, Chile4 Millennium Institute of Astrophysics, Santiago, Chile5 Astrophysics Group, Physics Department, University of the Western Cape, Private Bag X17, 7535 Bellville, Cape Town, South

Africa6 INAF - Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy7 INAF, Osservatorio Astronomico di Catania, via S.Sofia 78, 95123 Catania, Italy8 Dipartimento di Fisica, Universitá Federico II, Napoli, Italy9 Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK

10 Department of Physical Sciences, The Open University, Milton Keynes, MK7 6AA, UK11 ASI Science Data Center, via del Politecnico snc, 00133 Roma, Italy

Received: ????; Revised: ??????; Accepted: ?????

ABSTRACT

Aims. We describe the observing strategy, data reduction tools and early results of a supernova (SN) search project, named SUDARE,conducted with the ESO VST telescope aimed at measuring the rate of the different types of SNe in the redshift range 0.2 < z < 0.8.Methods. The search was performed in two of the best-studied extragalactic fields, CDFS and COSMOS, for which a wealth ofancillary data are available in the literature or public archives. We developed a pipeline for the data reduction and rapid identificationof transients. As a result of the frequent monitoring of the two selected fields we obtained light curve and colour information forthe transients sources that were used for the selection and classification of SNe by means of a especially developed tool. For theaccurate characterisations of the surveyed stellar population we exploit public data and our own observations to measure the galaxyphotometric redshifts and rest frame colours.Results. We obtained a final sample of 117 SNe, most of which are SN Ia (57%) and the remaining core collapse events of which44% type II, 22% type IIn and 34% type Ib/c. In order to link the transients, we built a catalog of ∼ 1.3 × 105 galaxies in the redshiftrange 0 < z ≤ 1 with a limiting magnitude KAB = 23.5 mag. We measured the SN rate per unit volume for SN Ia and core collapseSNe in different bin of redshifts. The values are consistent with other measurements from the literature.Conclusions. The dispersion of the rate measurements for SNe-Ia is comparable with the scatter of the theoretical tracks for single(SD) and double degenerate (DD) binary systems models, therefore the data do not allow to disentangle among the two differentprogenitor scenarios. However, we may notice that among the three tested models, SD and two flavours of DD, either with a steep(DDC) or a wide (DDW) delay time distribution, the SD gives a better fit across the whole redshift range whereas the DDC bettermatches the steep rise up to redshift ∼ 1.2. The DDW appears instead less favoured. The core collapse SN rate is fully consistent,unlike recent claims, with the prediction based on recent estimates of the star formation history, and standard progenitor mass range.

Key words. Stars: supernovae: general - Galaxies: star formation - Galaxies: stellar content - surveys

1. Introduction

The evolution of the SN rate with redshift provides the observa-tional link between the cosmic star formation history (SFH), theinitial mass function and the stellar evolutionary scenarios lead-ing to the explosions. Until recent times, the available measure-ments were limited to the local Universe and to sparse, some-times conflicting, high redshift measurements(e.g. Dahlen et al.2012; Maoz et al. 2014, and reference therein). The new gener-ation of panoramic detectors, now available in many observato-

? Based on observations made with ESO telescopes at the ParanalObservatory under programme ID 088.D-4006, 088.D-4007, 089.D-0244, 089.D-0248, 090.D-0078, 090.D-0079, 088.D-4013, 089.D-0250, 090.D-0081

ries, has substantially improved the survey capabilities and as aconsequence the number of SN searches and rate measurements.Most of the efforts were devoted to type Ia SNe whose progenitorscenario is still strongly debated but the interest for core collapseSNe (CC SN) is also growing.

The notion that measurement of the evolution of the SN Iarate with redshifts, in combination with measurement of theSFH, can be used to constrain the SN Ia progenitor scenarioswas first illustrated by Madau et al. (1998) (see also Sadat et al.1998; Ruiz-Lapuente & Canal 1998). Early measurements werepuzzling, showing a very rapid raise of the SN Ia rate up to red-shift ∼ 1 and then a decline at higher redshift (Dahlen et al. 2004;Barris & Tonry 2006; Dahlen et al. 2008). This implied a longdelay time from star formation to explosion for the SN Ia progen-

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itors, that appears conflicting with the indications derived fromthe rate measurement in the galaxies of the local Universe (Man-nucci et al. 2005). Subsequent measurements did not confirm theearly results, but the issue is still debated (e.g. Kuznetsova et al.2008; Rodney & Tonry 2010; Smith et al. 2012; Perrett et al.2012; Graur et al. 2014, and reference therein).

For what concerns CC SNe, the early measurements showthat, as expected, the rates tracks the cosmic star history (Botti-cella et al. 2008), though the scaling factor seems to be a factor2 smaller than what was expected from the SFH. A possible ex-planation is that many dim CC SNe, are missed by SN searches(Horiuchi et al. 2011) and/or part of the missing SNe may be hid-den in the dusty nuclear regions of star-burst galaxies (Dahlenet al. 2012, and reference therein). However, it is fair to say thatthe significance of the claimed discrepancy is still poor.

In addition, we note that in most cases, the cosmic SN rateswere derived from surveys designed to identify un-reddenedSN Ia for the cosmological distance ladder. In these cases thespecific observing strategy and/or candidate selection criteriamay introduce biases in the event statistics that can be difficult toproperly account. As a consequence, the SN rates derived fromthese surveys may be inaccurate.

In order to use the rate evolution to constrain the SN pro-genitor scenarios, the knowledge of the properties of the parentstellar population is of fundamental importance, so that the vol-ume of Universe searched for SNe needs to be characterized interms of the galaxy distribution as a function of redshift, massand star formation history.

Based on these considerations, we conceived a new SNsearch (Supernova Diversity and Rate Evolution, SUDARE, Bot-ticella et al. 2013), with primary goal to measure SN rates atmedium redshift, that is 0.2 < z < 0.8. To combine the require-ments of good statistics (> 200 events) and the availability ofancillary data for the surveyed fields, we planned for a four yearproject to monitor of two very well known extragalactic fields,the Chandra Deep Field South (CDFS) and the Cosmic Evolu-tion Survey (COSMOS) fields. Thanks to the long term com-mitment of many different observing programs, extended multi-band photometry is available for these fields. These data allow anaccurate characterization of the galaxy sample, which is crucialto infer general properties of the SN progenitors.

The present paper describes the SUDARE survey strategy,the procedures for the identification of transients, and the SNcandidate selection and classification after the first two years ofobservations. Then, we discuss the definition of the galaxy sam-ple and the procedure to derive photometric redshifts. Finally,we estimate the SN rates per unit volume at different redshiftsand compare them to published estimates. A detailed study ofSN rates as a function of different galaxy parameters will be pre-sented in a companion paper (Botticella et al., in preparation,hereafter PII).

Along the paper, we will adopt H = 70km s−1 Mpc−1, ΩM =0.3 and ΩΛ = 0.7. Magnitudes are in the AB system.

2. The survey

The SUDARE SN survey is performed using the VLT SurveyTelescope (VST, Capaccioli & Schipani 2011) equipped with theOmegaCAM camera (Kuijken 2011), that started regular opera-tion in October 2011 at ESO Paranal (Chile). The VST has aprimary mirror of 2.6 m and a f/5.5 modified Ritchey-Chretienoptical layout designed to deliver a large, uniform focal plane.The camera is equipped with a mosaic of 8 × 4 CCDs, eachwith 4k×2k pixels, covering 1 square degree with a pixel scale

of 0.214 ′′pix−1, allowing for an optimal sampling of the PSFeven in good seeing conditions. The thinned CD44-82 devicesfrom E2V have the advantage of an excellent quantum efficiencyin the blue bands but with the drawback that the i and z-bandssuffer for significant fringing contamination.

Most of the observing time at this facility is committed toESO public surveys1 but a fraction of the time is dedicated toGuaranteed Time Observations (GTO) made available to thetelescope and instrument teams in reward of their investmentsin the construction and installation of the instruments.

SUDARE is a four-year program and this paper is devotedto the analysis of first two observing seasons for VOICE-CDFSand one season for COSMOS. We are now completing the mon-itoring of both fields for the subsequent two seasons.

The time allocated to our project for monitoring VOICE-CDFS was from the VST and OMEGAcam GTO. The observingstrategy is to span 4 deg2 in four pointings, with one pointing foreach observing season (August to January). Here we present datafor two of these pointings.

To extend the photometric coverage, we implemented a syn-ergy with the VOICE (VST Optical Imaging of the CDFS andES1 Fields) project (Covone et al. in preparation). VOICE is aGTO program that has the aim to secure deep optical counterpartto existing multi-band photometry of selected fields. The multi-band catalog will be used to study the mass assembly and starformation history in galaxies by combining accurate photomet-ric redshifts, stellar masses and weak lensing maps.

The monitoring of the COSMOS field relies instead on a pro-posal submitted for ESO VST open time (P.I. Pignata). For thisfield we maintained the same pointing coordinates from one sea-son to the next. This allows us the detection of transients withvery long time evolution, but, due to the limited area probed, thedata may be prone to cosmic variance.

The survey strategy consists of monitoring the selected fieldsevery three days in the r-band, excluding only ±5 days aroundfull moon. The exposure time is 30 min with the aim of reachinga magnitude limit of 25th in average sky conditions. Each obser-vation is split into five 6 min exposures with a dithering patterndesigned to fill the gaps between the detector chips (that rangefrom 25 to 85 arcsec). Because of the dithering, the effective areacovered by combining the exposures of a given field is 1.15 deg2,although with a reduced signal to noise ratio at the edge.

With a more relaxed cadence (3-4 times per month), we alsoplanned for g and i-band exposures. With these observations wecan measure the colour of the transients that is essential for theirphotometric classification and to obtain an estimate of the ex-tinction along the line of sight. Adapting to the rules for theobserving blocks in service mode at ESO, the planned observ-ing sequence is g-r, r, r-i with a three-day interval between eachblock. To ensure a good image quality we required a maximumseeing of 1.2 ′′ (FWHM) at the beginning of the exposure. This,along with the obvious requirement of clear sky, implied that theactual epochs of observations often deviate from the ideal planmainly because of unsuitable sky conditions.

Table 1 lists for each field, the pointing coordinates alongwith the field size, the observing season, the number of availableepochs in the different bands and the range and median valueof the seeing measured for the r-band exposures. The full log ofobservations is given in Tab. 2 where, for each epoch, we list theseeing (in arcsec) and m50, the magnitude corresponding with atransient detection efficiency of 50% (cf. Sect. 3).

1 http://www.eso.org/public/teles-instr/surveytelescopes/vst/surveys/

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Table 1. Field coordinates and compact log of observations

field R.A. (2000.0) Dec. field size observing epochs seeinghh:mm:ss dd:mm:ss deg2 season r g i range [median]

VOICE-CDFS1 03:33:34.506 -27:34:10.78 1.15 Aug2012-Jan2013 29 7 11 0.51-1.44 [0.89]VOICE-CDFS2 03:29:02.654 -27:34:00.70 1.15 Oct2011-Jan2012 23 6 4 0.51-1.46 [0.82]COSMOS 10:00:28.600 +02:12:21.00 1.15 Dec2011-Apr2012 28 7 7 0.50-1.20 [0.84]

Table 2. Log of observations. For each field we report the epoch of observation (civil date and MJD), the seeing (FWHM in arcsec and m50 themagnitude corresponding with a transient detection efficiency of 50%.)

VOICE-CDFS1 VOICE-CDFS2 COSMOSEpoch MJD seeing m50 Epoch MJD seeing m50 Epoch MJD seeing m50

r r r2012-08-05 56144.38 1.31 23.0 2011-10-20 55854.15 1.17 23.2 2011-12-18 55913.30 0.65 23.22012-08-13 56152.37 0.69 23.9 2011-10-25 55859.34 0.56 23.8 2011-12-22 55917.27 0.92 23.52012-09-02 56172.23 1.02 21.7 2011-10-28 55862.16 0.92 23.5 2011-12-27 55922.24 1.03 23.32012-09-05 56175.20 1.28 21.8 2011-10-30 55864.16 1.06 23.4 2011-12-31 55926.28 1.14 23.32012-09-08 56178.31 1.00 23.0 2011-11-02 55867.10 0.78 23.5 2012-01-02 55928.32 0.63 23.72012-09-14 56184.25 0.55 23.9 2011-11-04 55869.15 0.62 23.4 2012-01-06 55932.30 0.58 23.72012-09-17 56187.29 1.06 23.2 2011-11-15 55880.06 0.61 23.6 2012-01-18 55944.20 0.63 23.72012-09-20 56190.22 0.87 23.1 2011-11-18 55883.29 0.90 23.4 2012-01-20 55946.25 0.87 23.42012-09-22 56192.22 0.89 23.1 2011-11-21 55886.23 0.68 23.5 2012-01-22 55948.25 0.77 23.62012-09-24 56194.24 1.44 22.6 2011-11-23 55888.28 0.90 23.4 2012-01-24 55950.28 0.68 23.82012-10-07 56207.34 0.93 23.1 2011-11-26 55891.28 0.64 23.8 2012-01-27 55953.19 0.92 23.52012-10-08 56208.24 0.93 22.9 2011-11-28 55893.30 1.04 23.2 2012-01-29 55955.20 0.86 23.32012-10-11 56211.32 0.92 23.1 2011-12-01 55896.22 0.82 23.6 2012-02-02 55959.31 0.88 23.32012-10-14 56214.71 1.07 23.7 2011-12-03 55898.24 0.52 23.8 2012-02-16 55973.25 0.50 23.62012-10-17 56217.21 0.92 23.7 2011-12-14 55909.21 0.88 23.3 2012-02-19 55976.11 0.97 23.32012-10-21 56221.19 0.51 23.8 2011-12-17 55912.25 0.88 23.2 2012-02-21 55978.15 0.77 23.62012-10-25 56225.11 0.86 22.9 2012-01-14 55940.15 0.77 23.3 2012-02-23 55980.18 0.74 23.62012-11-04 56235.14 0.67 23.6 2012-01-18 55944.06 0.57 23.9 2012-02-26 55983.12 0.84 23.72012-11-06 56237.26 0.83 23.5 2012-01-20 55946.134 1.00 23.4 2012-02-29 55986.040 0.89 23.22012-11-08 56239.27 0.88 23.6 2012-01-23 55949.11 0.59 23.5 2012-03-03 55989.196 0.93 23.22012-11-10 56241.30 0.76 23.6 2012-01-25 55951.12 0.90 23.7 2012-03-06 55992.115 0.80 22.62012-11-20 56251.14 0.78 24.0 2012-01-29 55955.06 0.67 23.6 2012-03-13 55999.033 0.68 23.52012-12-03 56264.21 0.71 23.4 2012-02-02 55959.08 1.46 22.4 2012-03-15 56001.045 1.11 23.12012-12-07 56268.05 0.81 23.8 g 2012-03-17 56003.05 0.92 23.32012-12-13 56274.06 0.55 23.8 2011-11-02 55867.12 0.91 23.4 2012-05-08 56056.000 0.71 23.42012-12-20 56281.13 0.96 23.3 2011-11-21 55886.25 0.89 23.5 2012-05-11 56058.10 0.85 23.32013-01-03 56295.10 0.68 23.8 2011-12-01 55896.25 1.05 23.7 2012-05-17 56064.03 0.75 23.52013-01-06 56298.13 0.91 23.7 2012-01-18 55944.09 0.56 23.6 2012-05-24 56071.07 1.20 22.82013-01-10 56302.12 0.89 23.0 2012-01-25 55951.15 1.56 23.2 g

g 2012-12-08 56269.26 1.02 23.4 2011-12-27 55922.26 1.13 23.52012-09-20 56190.24 1.17 23.6 i 2012-01-22 55948.27 1.11 23.62012-10-11 56211.35 1.13 23.4 2011-11-02 55867.15 0.59 23.1 2012-02-02 55959.33 0.88 23.82012-10-21 56221.22 0.57 23.7 2011-11-21 55886.28 1.12 22.7 2012-02-16 55973.28 0.61 23.92012-11-06 56237.28 0.91 23.6 2011-12-01 55896.27 0.92 22.8 2012-02-26 55983.14 1.04 23.82012-12-07 56268.07 0.82 24.1 2012-01-18 55944.11 0.69 22.9 2012-03-17 56003.07 1.04 23.62012-12-09 56270.28 1.08 23.5 2012-05-09 56056.02 0.78 23.82013-01-06 56298.16 1.14 23.7 i

i 2011-12-27 55922.28 0.93 24.12012-08-13 56152.39 0.54 23.0 2012-01-22 55948.30 0.95 23.22012-09-02 56172.26 0.98 21.1 2012-02-02 55959.36 1.05 22.72012-09-08 56178.34 1.45 22.0 2012-02-16 55973.30 0.60 23.32012-09-17 56187.31 0.91 22.7 2012-02-26 55983.17 0.88 24.12012-09-24 56194.27 1.42 22.2 2012-03-17 56003.10 0.53 24.22012-10-15 56215.19 0.89 22.9 2012-05-09 56056.05 0.62 23.42012-10-25 56225.14 0.85 22.22012-11-08 56239.29 0.84 22.82012-11-20 56251.16 0.69 23.22012-12-20 56281.15 0.79 22.72013-01-10 56302.15 0.97 22.0

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The SN search was complemented by 3 runs of one nighteach at the ESO-VLT for the spectroscopic classification of adozen candidates. These observations, described in Sect. 4.3,were intended as spot checks of the SN photometric classifica-tion tool (Sect. 4).

We remark that, as by-product, the SUDARE data archivewas also used to explore the performances and completeness ofAGN detection via variability (De Cicco et al. 2015; Faloccoet al. 2015).

2.1. Image calibration

The raw data were retrieved from the ESO archive and trans-ferred to the VST data reduction node in OAC-Naples. Here thefirst part of the data reduction is performed using the VSTTubepipeline (Grado et al. 2012). A description of the VSTtube datareduction process is reported in De Cicco et al. (2015). In short,the pipeline first performs flat fielding, gain harmonisation andillumination correction and all images for a given field are reg-istered to the same spatial grid and photometric scale. Finallythe dithered images for one epoch are median averaged produc-ing one stacked image. The pipeline also delivers weight pixelmasks tracking for each pixel the number of dithered exposurescontributing to the combined image after accounting for CCDgaps, bad pixels and cosmic rays rejection.

The pipeline was also used to produce deep stacked imagesby combining all the exposures in a given filter with the bestimage quality, those with seeing ≤ 0.8′′. These stacked images,reaching a limiting magnitude ∼ 1mag fainter than good sin-gle epoch exposures (the 3-σ magnitude limits are 26.2, 25.6,24.9 mag for r,g and i-band respectively) were used to extractgalaxy photometry to complement the public multi-band cata-logs. (Sect. 6).

2.2. Transient detection

For the detection of transient sources and the selection of SNcandidates, the mosaic images were processed with an ad-hocpipeline. This is a collection of python scripts that makes useof pyraf and pyfits2 tasks and incorporate other publicly avail-able software for specific tasks, in particular SExtractor (Bertin& Arnouts 1996) for source detection and characterisation, hot-pants3 for PSF match and image difference, daophot (Stetson1987) for accurate point spread function (PSF) fit photometry,stilts4 for catalog handling and mysql for the transient database.The flowchart of the SUDARE pipeline is the following:

1. We produced a mask for saturated stars that is combined withthe weight map produced by VSTTube, to build a bad pixelmask for each mosaic image. Those flagged as bad pixelswere excluded from further analysis.

2. For each image we computed the difference from a selectedtemplate image. This required the derivation of the convolu-tion kernel matching the PSF of the two images. The methodis described in Alard (2000) though we used the hotpantsimplementation. We note that for the first observing seasonwe do not have earlier templates and we therefore used astemplates, images acquired on purpose a few months aftercompletion of the transient survey campaign.

2 http://www.stsci.edu/institute/software_hardware/3 A package provided by A. Becker (http://www.astro.washington.edu/users/becker/v2.0/hotpants.html)4 http://www.star.bris.ac.uk/~mbt/stilts/

3. The transient candidates were identified, using SExtractor,as positive sources in the difference image. Depending onthe image quality and detection threshold, the candidate liststarts with several thousands objects for each epoch. Mostdetections are artefacts due to poorly masked CCD defects,poorly removed cosmic rays, residual from the subtraction ofbright sources, reflection ghosts from bright sources, etc.

4. The transient candidates were ranked based on a customalgorithm that uses a number of measured sextractor met-rics of the detected sources. The most informative param-eters and the ranking scores were selected and calibratedthrough extensive artificial star experiments. In these exper-iments, a number of fake stars were placed in the search im-age that is then processed through the detection and rankingpipeline. The success rate of artificial star recovery was com-pared to the number of residual spurious sources. We foundthat the most informative parameters are the source FWHM,f lux_ratio, isoarea and magnitudes, measured at differentapertures. With a proper selection of these parameters, wecan drastically reduce the number of spurious events whilelimiting the number of good candidates improperly rejected.The performance of the ranking algorithm depends on theimage quality. On average, we found that we can eliminate∼ 95% of the spurious transients at the cost of losing ∼ 5%of good candidates. Correction for the lost SNe is incorpo-rated in the detection efficiency (c.f. Sec. 3) since we usedthe same algorithm to select real and fake SNe. After thisselection, typically a hundred candidates per field and epochare left to the next step of human inspection and validation.

5. To associate each transient to its possible host galaxy, wecross correlated the transient list with the galaxy catalogsderived from the deep r-band stacked image (cf. Sec. 6). Agalaxy is adopted as the host for a given transient when thelatter appears engulfed in the galaxy boundaries. The bound-aries are those of the ellipse defined by the SExtractor pa-rameters CXX, CYY and CXY through the equation:

CXX(x − xc)2 + CYY(y − yc)2 + CXY(x − xc)(y − yc) = R2

where xc, yc is the galaxy center and following the SExtrac-tor’s manual, we assume that the isophotal limit correspondsto R = 3.In four cases the transient/host galaxy pairing was ambigu-ous because of the overlap of the ellipses of different galax-ies. In these cases we also considered the consistency of thehost galaxy redshift with the indication of SN photometricclassification (c.f. Sect. 4).Only for two transients no counterparts was detected in thedeep stacked image (cf. Tab. 4).

6. The available information for the best ranked candidates areposted on WEB pages where the user can inspect the imagesand candidate metrics for the search and template epochsand selects the good candidates assigning each of them apreliminary classification according to different classes (SN,AGN, variable star, moving object). The selected candidatesare then archived in a mysql database.

7. For all selected candidates, we derive accurate light curvesmeasuring the source magnitude at all available epochs. Wemeasure both aperture and PSF-fit photometry on the origi-nal search images and on the difference images. We verifiedthat the PSF-fit gives more reliable measurements that plainaperture integration mainly because PSF photometry is lesssensitive to the background noise.

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The transient search process was performed in the r-band foreach epoch. Because of the dense temporal sampling, in gen-eral good SN candidates will have multiple detections in thedatabase. In principle, we can easily implement a candidate se-lection based on multiple occurrences of a given source in thedatabase that would further reduce spurious candidates. How-ever, at the present stage of the project we adopted a conserva-tive approach accepting the burden of the visual examination ofmany candidates to maximise the completeness fraction.

3. Detection efficiency

To derive the SN rates we need to obtain an accurate estimateof the completeness of our search. This is done by extensive ar-tificial stars experiments exploring a range of magnitudes andpositions on the images.

For every search image, we first obtain the PSF from theanalysis of isolated field stars. Then, a number of fake stars ofa given magnitude, generated scaling the PSF and adding theproper Poisson noise, were injected on the search image. Tomimic the range of properties of real sources, three different cri-teria were adopted for positioning the fake stars, with roughlythe same number of star for each class. The three classes are:

– events associated with galaxies. From the source catalog onthe field (cf. Sect. 6) we picked up a random sample of galax-ies and one fake star was placed in each of them. The positioninside the galaxy was chosen randomly following the distri-bution of r-band flux intensity.

– events coincident with persistent, point-like sources. Fakestars were added at the same position of existing sources inthe field. This mimics SNe in the nucleus of compact hostgalaxies, variable AGNs and variable stars.

– events with no counterpart in the template image. These wereplaced at random positions across the field of view irrespec-tive of existing sources or to the possible coincidence withCCD defects or gaps.

The images with fake stars were processed with the searchpipeline and the number of detected events surviving after theranking procedure were counted. The fraction of detected overinjected events gives the detection efficiency for the given mag-nitude. The experiment is repeated sampling the magnituderange of interest (18 < r < 26) to derive the detection efficiencyas a function of magnitude.

We inject 500 fake stars per experiment per image and repeatthe experiment 5 times for a given magnitude. In fact, addinga large number of fake stars could bias the computation of theconvolution kernel, hence the image subtraction process, makingthe experiments less reliable.

An example of the derived detection efficiencies as a functionof magnitude for one epoch and field is shown in Fig. 1, wherethe errorbars show the dispersions out of three experiments. Ascan be seen in Fig. 1, the detection efficiency as a function ofmagnitude can be represented by the following analytical func-tion:

DE = DEmax

(arctan β (mag50 − mag)

π+

12

)(1)

where DEmax is the maximum value of the detection effi-ciency, mag50 is the magnitude corresponding with the 50% dropof the detection efficiency and β measures the decline rate of the

19 20 21 22 23 24 25mag

0.0

0.2

0.4

0.6

0.8

1.0

dete

ctio

n e

ffic

iency

DEmax

mag50

Fig. 1. Transient detection efficiency as a function of magnitude forthe r-band observation of COSMOS on 2012/03/15. The dots are theaverages of three artificial star experiments with the errorbar being thedispersion whereas the line is the adopted efficiency curve after the fitwith the Eq. 1 (DEmax = 93%, mag50 = 23.0 mag and β = 6.1).

DE. The best fit parameters are determined through least squareminimisation of the residuals (e.g. Fig. 1). Note that the maxi-mum detection efficiency is ∼ 95%, even at the bright magnitudeend. This is because not all the pixels of the image are useful andindeed the bad pixel mask (cf. step 1 of the SUDARE pipeline)flags 5 − 10% of the image area.

We verified that the detection efficiencies measured indepen-dently for each of the three classes of fake stars described aboveare similar with a dispersion of mag50 values of ∼ 0.3 mag andhereafter we will use the average values. Also, we found that thevalues of DEmax and β are very similar for each epoch and fieldwith a mean value of 95% ± 3 and 4 ± 2, respectively.

The artificial star experiment described above was repeatedfor all epochs and fields of the search, and the resulting detectionefficiencies are used in the rate calculation.

4. Transient classification

The result of the transient search was a list of ∼ 350 SN candi-dates. A fraction of these transients are coincident with persis-tent sources and therefore can be SNe in the nucleus of the hostgalaxies but also variable stars or AGNs. In fact, after the anal-ysis of Falocco et al. (2015) and De Cicco et al. (2015), threecandidates with slow evolving light curves were found to co-incides with X-ray sources. These were classified as AGN andremoved from the SN candidate sample. We also removed fromthe SN candidate list all transients with spectroscopic or photo-metric redshift z > 1 (∼ 25%).

We used the measured light curve and colour evolution toconstrain the nature of the transients and to classify SNe in theirdifferent types. Conservatively, only candidates with at least 5photometric measurements at different epochs (even in differentfilters) were considered. Because of the frequent monitoring ofour survey, this criterium excludes only four candidates with anegligible effect on the SN counts.

The photometric classification is more reliable if the redshiftof the host galaxy is available. When the transient is not associ-ated with a host galaxy or when the host redshift is not available,

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the redshift is left as a free parameter in the transient light curvefitting (see next section).

4.1. Photometric classification of SNe

For the photometric classification we used a tool developedfor the SUDARE project. The tool compares the SN candidatemulti-color light curves with those of SN templates, and iden-tifies the best-matching template, redshift, extinction and lumi-nosity class. The tools was developed following the strategy ofthe SN classification tool PSNID (Sako et al. 2011). We devel-oped our own tool because we want to explore different priorsfor the fitting parameters and different classification scheme.

We collected a sample of templates for different SN typesfor which both multicolour light curves and sequence of spectraare available (Tab. 3). The spectra are needed to estimate the K-correction. The templates were retrieved from a database of SNlight curves and spectra that we have collected in the study ofSNe at ESO and ASIAGO Observatory (the template spectra canbe downloaded also from WISEREP5, the SN spectra database;Yaron & Gal-Yam 2012).

The templates were selected to represent the well establishedSN types, namely Ia, Ib, Ic, IIb, II Plateau and Linear, IIn, withthe addition of representative peculiar events (see individual ref-erences for details). In particular, we included SN 2008es as rep-resentative of the recently discovered class of very luminous SNe(SLSN, Quimby et al. 2007, 2013; Gal-Yam 2012) that, althoughintrinsically very rare, may be detected in high redshift searchesbecause of their large volume sampling.

The steps of the photometric typing are:

– for each template, we derived K-correction tables as a func-tion of phase from maximum and redshift (in the range0 < z < 1). K-corrections are obtained as the difference ofthe synthetic photometry measured on the rest frame spec-tra and on the same spectra properly redshifted. The redshiftrange for which we can derive accurate K-correction is lim-ited by the lack of UV coverage from most templates. Thisis a problem in particular for the g-band where we are forcedto accept uncertain extrapolations.

– the K-corrected light curves of template SNe were used topredict the observer frame light curves in the gri-bands ex-ploring the 0 < z < 1 redshifts range and the −0.3 < EB−V <1 mag extinction range (the negative lower limit for the EB−Vrange allows for uncertainties in the template extinction cor-rection and for variance in the intrinsic SN colour). With thegoal to minimise the uncertainties on the K-correction, thetemplate input band was taken to best match the observerframe band for the given redshift, e.g. we use the templateV, B,U bands to predict the observer frame r-band light curveof SNe at redshift z ∼ 0.1, 0.4, 0.7, respectively.

– we estimate the goodness of the fit of the template to theobserved light curve computing the sum of the square of fluxresiduals weighted by the photometric errors (χ2) for eachsimulated light curve of the grid. Besides the redshift andextinction ranges we explore a range of epochs of maximum,Tmax (the initial guess is the epoch of the observed r-bandbrightest point) and of intrinsic luminosities, ∆(µ) (allowingfor a ±0.3 mag flux scaling of the template). The residualsfor all bands are summed together and therefore each bandcontributes to the overall χ2 with a weight proportional to thenumber of measurements.

5 http://wiserep.weizmann.ac.il

– for the selection of the best fitting template we use Bayesianmodel selection (e.g. Poznanski et al. 2007a; Kuznetsova &Connolly 2007; Rodney & Tonry 2009). In particular fol-lowing Sako et al. (2011) we compute for each SN type theBayesian evidence:

Etype =∑

template

∫pars. range

P(z) e−χ2/2dz dAV dTmax d∆(µ)

where the fitting parameters are the redshift z, with P(z) itsprobability distribution, the extinction AV , the time of maxi-mum Tmax and the flux scaling factor ∆(µ).The spectroscopic redshift is used as a prior if available andin this case for P(z) we adopt a normal distribution centeredat the spectroscopic redshift and with σ = 0.005. Other-wise, if a photometric redshift estimate is available, we useas redshift prior the P(z) provided by the photometric red-shift code (cf. Sect. 6.3.1). In the worst case, either whenthe host galaxy is not detected, or when the photometric red-shift is poorly constrained, we adopt a flat prior in the range0 < z < 1.In all cases we adopted flat prior for the extinction distribu-tion and for the flux scaling.More critical is choice of templates. As emphasised by Rod-ney & Tonry (2009), the Bayesian approach relies on an ap-propriate template list that should be as complete as possiblebut, at the same time, avoid duplicates. When the templatelist includes rare, peculiar events, especially if they mimicthe properties of a more frequent SN type, it is appropriateto use frequency priors. Alternatively, for specific applica-tions one may exclude ambiguous cases or rare, peculiar SNtypes (c.f. Sako et al. 2011) from the template list.Our template list, given in Tab. 3, is intended to representthe full range of the most frequent SN type with a number oftemplates for each class that is broadly consistent with theirfrequency in a volume limited SN sample (Li et al. 2011b).After that we adopted flat priors for the relative rate of eachtemplate within a given class and for the relative rates of thedifferent SN types.We computed the Bayesian probability for each of the mainSN types as:

Ptype =Etype

EIa + EIb/c + EII + (ESLSN)

Notice that for the purpose to assign probability, we mergedregular type II and type IIn templates. However, in the sub-sequent analysis we indicated when the best fitting template(the one with the highest probability) is a type IIn. Also, afterverifying that none of our candidate has a significant proba-bility of matching a SLSN, the corresponding template wasdropped from the fitting list and the ESLSN term in Eq. 4.1cancelled. This was done to allow a direct comparison withSNANA (see next Section).

– for the most probable SN type, we record the best fitting tem-plate along with the fit parameters corresponding to the χ2

minimum. Of the about 250 transients, 117 were classifiedas SNe. Most of the remaining have erratic light curves con-sistent with that of AGNs.An example of the output of the SN typing procedure isshown in Fig. 2.

Table 4 lists our SNe; for each event we report the coordi-nates, the host galaxy redshift if available (col. 4), for photomet-ric redshift, the 95% lower and upper limits of the P(z) (col. 5),

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0 50 100 phase [observer frame]

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cosmos_r_20111218_20120508_1A

1991T Ia z=0.31 max=55907.3 AV=0.5 dm=0.2 chi=0.57 P=3.9e-02

0 50 10025

24

23

22

i [V]

Fig. 2. Example of the output of the SN typing procedure. The top panelshows the observed r-band light curve that in this case is compared withthe template K-corrected B-band light curve. The bottom panels showthe observed light curve and template fit for the the g-band (left) andi-band (right). Blue dots are the SN candidate observed magnitudes (ar-rows indicate upper limits) while red open circles are the template pho-tometry. The legend identifies the best fitting template and parameters.

the method of redshift measurement (col. 6) the most probableSN type (col. 7) and corresponding Bayesian probability (col 8),the best fitting template (col. 9), redshift (col. 10), extinction(col. 11), flux scaling (col 12) and epoch of maximum (col. 13).We also list the χ2

n (col. 14), the number of photometric mea-surements with, in parenthesis, the number of measurement withS NR > 2 (col. 15) and the integrated right tail probability of theχ2 distribution (Pχ2 , col 16).

In a number of cases the Pχ2 probability is fairly low (15 SNehave Pχ2 < 10−4). In some cases this is due to one/two deviantmeasurements, in other cases there is evidence of some variancein the light curve not fully represented by the adopted templateselection. We have to consider the possibility that these eventsare not SNe.

Also for some candidates with a small χ2, the number of ac-tual detections (photometric measurements with signal to noiseratio S NR > 2) is also small that it is not possible to definitelyassess the SN nature of the transient (for 12 candidates the num-ber of detection is Ndet <= 7).

To these probable SNe (indicated with PSN in the last col-umn of Tab. 4) we will attribute a weight 0.5 in the rate calcula-tion. The impact of the arbitrary thresholds for Pχ2 and the Ndetand the adopted PSN weight will be estimated in Sect. 7.3.

To evaluate the uncertainties of our classification tool in thenext two sections we compare our derived SN types with a)photometric classifications obtained using the public softwarepackage SNANA (Kessler et al. 2009) and b) with the spectro-scopic observations of a small sample of "live" transients, ob-served while still in a bright state.

Table 5. Comparison of photometric classification with the differenttools. In parenthesis we report the events labelled as probable SNe. Forcolumn 2 and 3 we count the SN with respect to the most probable SNtype. Column 4 lists instead sum the Bayesian probability for each SNtype (in parenthesis is the number of PSN).

SUDARE SNANA BayesianIa 67 72 64.7 (12.7)II 22 21 23.2 (6.5)IIn 11 7 10.7 (2.9)Ib/c 17 15 18.6 (4.8)All 117 115 117.0 (27.0)

4.2. Comparison with the photometric classifications byPSNID in SNANA

With the aim to check our procedure and evaluate the relateduncertainties, we performed the photometric classification of ourSN candidates using the public code PSNID in the SNANA6

Also for the fit with PSNID we set the host galaxy redshift asa prior with the same range of uncertainty as in our procedure.In this case however, also for photometric redshift we assume anormal distribution for P(z) with the σ provided by the photo-metric redshift code.

A comparison of the classifications obtained with the twotools is illustrated in Fig. 3. The pie chart shows the SN clas-sifications in the four main types using the SUDARE tool andthe sectors with different colours within a given wedge show thePSNID classifications. For a two events, marked in grey, the fitwith PSNID fails.

The figure shows that the identification of SN Ia is quite con-sistent (92% of the type Ia classified by our tool are confirmedby PSNID) and there is a good agreement also for the normaltype II (77% of the classifications are matched). The agreementis poor for type Ib/c and for type IIn (only 40% are matchedin both cases). The latter result is not surprising considering thewide range of luminosity evolution: for these classes of SNe thechoice of input templates is crucial.

However, we remark that despite the discrepancy in the clas-sification of individual events of specific sub-types, there is anexcellent agreement of the event counts in each class, but fortype IIn, as shown in Tab. 5. This implies that, as far as the SNrates are concerned, using either classification tools makes a lit-tle difference, with the exception the exception of type IIn wherethe difference is ∼ 40%.

In Tab 5 we also report the SN count for the different classesusing the Bayesian probability. It appears that with respect to thecount of the most probable type, the number of SN Ia is slightlyreduced while the number of Ib/c increases. This is not unex-pected given the similarity of the light curves of type Ia and Ib/c(in many cases an event can have a significant probability of be-ing either a type Ia or a type Ib/c) and the fact that type Ia are in-trinsically more frequent than Ib/c. The effect however is small,< 5% in both cases.

6 http://das.sdss2.org/ge/sample/sdsssn/SNANA-PUBLIC.Weused\protect\relax\protect\relax\protect\edefnit\protect\xdef\T1/txr/m/n/6\T1/txr/m/n/9\T1/txr/m/n/6\size@update\enc@updateSNANAversion10_39k.implementation(Sako et al. 2011; Kessler et al. 2009). Overall, the approach of PSNIDis similar to that adopted here; besides the implementation of thecomputation algorithm, the main difference is in the template list. Inparticular, for SN Ia we adopted the fitting set-up of Sako et al. (2011),while for CC SNe we used the extended list of 24 templates availablein the SNANA distribution7

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Table 3. List of template SNe used for the SUDARE’s SN photometric classification tool

SN type vel m − M reference[km s−1]

1990N Ia 998 31.73 Lira et al. (1998); Mazzali et al. (1993)1992A Ia 1845 31.14 Kirshner et al. (1993); Altavilla et al. (2004)1994D Ia 450 30.92 Richmond et al. (1995); Patat et al. (1996)2002bo Ia 1289 31.77 Benetti et al. (2004)1999ee Ia bright 3407 33.42 Stritzinger et al. (2002);Hamuy et al. (2002)1991T Ia bright 1732 30.74 Lira et al. (1998); Altavilla et al. (2004); Ruiz-Lapuente et al. (1992)1991bg Ia faint 913 31.44 Filippenko et al. (1992); Leibundgut et al. (1993); Turatto et al. (1996)2000cx Ia pec 2421 32.39 Candia et al. (2003); Matheson et al. (2008)2002cx Ia pec 7183 35.09 Li et al. (2003)1987A II 320 18.48 Catchpole et al. (1989); Hamuy et al. (1990); ESO/Asiago Archive1992H IIL 1021 30.97 Clocchiatti et al. (1996a)2009bw IIP 1155 31.45 Inserra et al. (2012)1999em IIP 710 29.47 Hamuy et al. (2001); Leonard et al. (2002a); Elmhamdi et al. (2003)2004et IIP 40 28.85 Maguire et al. (2010)1999br II faint 1021 30.97 Pastorello et al. (2004)1999gi II faint 592 29.80 Leonard et al. (2002b)2005cs II faint 600 29.26 Pastorello et al. (2006, 2009)1993J IIb -35 27.80 Filippenko et al. (1992); Richmond et al. (1994); Barbon et al. (1995)2008ax IIb 579 29.92 Pastorello et al. (2008); Taubenberger et al. (2011)1997cy II pec 17700 37.03 Turatto et al. (2000)1998S IIn 895 31.18 Fassia et al. (2000)2010jl IIn 3207 34.92 Pozzo et al. (2004)2005gj IIn (Ia) 17988 37.15 Aldering et al. (2006)2008es SLSN-II 0.202* 39.70 Gezari et al. (2009)2009jf Ib 2379 32.65 Valenti et al. (2011)2008D Ib (XRF) 1955 32.29 Mazzali et al. (2008)1994I Ic 461 29.60 Wheeler et al. (1994); Clocchiatti et al. (1996b)1998bw Ic (GRB) 2550 32.76 Galama et al. (1998); Patat et al. (2001)2004aw Ic 4742 34.17 Taubenberger et al. (2006)2007gr Ic 492 29.84 Valenti et al. (2008); Hunter et al. (2009)

* redshift instead of velocity

SNANA

IaIIIInIbcunkw

Ia

II IIn

Ibc

Fig. 3. Comparison of the SN classifications obtained with the differenttools. The exploded wedges are the SN type fractions obtained with ourSUDARE tool and the coloured sectors are the SNANA classification.

4.3. Comparison with spectroscopic classification

For a small sample of the SN candidates we obtained immediatespectroscopic classification.

Observations were scheduled at the ESO VLT telescopeequipped with FORS2 at three epochs for a total allocation of2 nights. The telescope time allocation, that was fixed severalmonths in advance of the actual observations, dictated the choiceof the candidates: we selected transients that were "live" (abovethe detection threshold) at the time of observations and amongthese we gave a higher priority to the brightest candidates withthe aim to secure a higher S/N for the spectra.

For the instrument set-up we used two different grisms,namely GRIS_300V and GRIS_300I covering the wavelengthrange 400-900 nm and 600-1000 nm respectively with similarresolution of about 1 nm. The choice of the grism for a particu-lar target was based on the estimated redshift of the host galaxywith the GRISM_300I used for redshift z > 0.4.

We were able to take the spectrum of 17 candidates. Spectrawere reduced using standard recipes in IRAF. In three cases theS/N was too low for a conclusive transient classification and wewere only able to obtain the host galaxy redshifts. Four of thecandidates turned out to be variable AGNs, in particular Seyfertgalaxies at redshifts between 0.25 < z < 0.5. We stress that,to maximise the chance of obtaining useful spectra, we tried toobserve the candidate shortly after discovery. This means that atthe time of observations we had not yet a full light curve andhence a reliable photometric classification. Eventually, all the

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6000 7000 8000 9000wavelenght [

A]

0.0

0.5

1.0

1.5

2.0

flux

Fig. 4. The observed spectrum of SN 2012gs obtained with FORS2 onMJD 56252.0 (black line) is compared with that of the SN Ic 2007gr atphase −9d (top) and of the type Ia SN 1991T at phase +14d (bottom).In both cases it is adopted for SN 2012gs a redshift z = 0.5 as measuredfrom the narrow emission lines of the host galaxy.

four AGN exhibit a erratic luminosity evolution that, if knownat the time of spectroscopic observations, would have allowedus to reject them as SN candidates.

Ten transients were confirmed as SNe and their spectral typewere assigned through cross-correlation with libraries of SNtemplate spectra using GEneric cLAssification TOol (GELATO,Harutyunyan et al. 2008) and the Supernova Identification code(SNID, Blondin & Tonry 2007). The spectroscopically classifiedSNe, identified with a label in Tab. 4, turned out as 6 type Ia, 2type Ic, 1 type II and a type IIn. In all cases the SN type wascoincident with the independent photometric classification withone exception (SN 2012gs) classified Ic from spectroscopy andIa from photometry.

As shown in Fig. 4, the spectrum of SN 2012gs can be fit-ted both by a template of type Ic SN well before maximum orby a type Ia SN two weeks after maximum, in both cases theredshift was z ∼ 0.5. On the other hand, when we consider thelight curve (Fig. 5) it results that the spectrum was obtained twoweeks after maximum and therefore the first alternative can nowbe rejected. Therefore, revising the original spectroscopic clas-sification, SN 2012gs is classified as type Ia.

4.4. Classification uncertainties

The comparison of the photometric and spectroscopic classifica-tions, even if for a very small sample, confirms that photometrictyping is a reliable in particular when the redshift of the hostgalaxy is known. For our photometric tools we did not yet per-formed a detailed testing that instead has been performed forPSNID. In particular, Sako et al. (2011) show that PSIND canidentify SN Ia with a purity of 90%. This appears consistentwith the results obtained from the comparison of PSNID andSUDARE tools. More difficult is to quantify the performancesof photometric classification for CC SN both because of the lackof suitable spectroscopic samples (Sako et al. 2011) and for thelimitation of simulated samples (Kessler et al. 2010). From thecomparison of the CC SN classification of the PSNID and SU-DARE tools we found differences in the individual classificationsof 25% for type II events and 40% for type Ib/c events. These

100 50 0 50 phase [observer frame]

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cdfs1_r_20121021_20120813_15A

1991T Ia z=0.53 max=56230.1 AV=0.3 dm=-0.2 chi=1.86 P=7.7e-03

100 50 0 50262524232221

i [B]

Fig. 5. SN 2012gs light curve fit obtained using our tool. The best matchis obtained with SN 1991T and the maximum is estimated to occur onMJD 56235.1.

21 22 23 24discovery mag

0

5

10

15

20

25

30

35

40

Fig. 6. Histogram of the r-band SN magnitudes at discovery. The dottedline shows the distribution of m50 (the magnitude where the detectionefficiency is 0.5) for the r-band observations (as reported in Tab 2).

should be considered as lower limit of the uncertainty becausethe two codes adopt similar approaches the main difference be-ing the choice of templates. On the other hand, the discrepancyon the overall SN counts of a given type is much lower, typicallya few percents, though for type IIn it is about 40%. Based onthese considerations and waiting for a more detailed testing weadopt the following uncertainties for SN classification: 10% forIa, 25% for II, 40% for Ib/c and IIn.

5. The SN sample

As a result of the selection and classification process, we ob-tained a sample of 117 SNe, 27 of which are marked as probableSNe (PSNe). The distribution of the SN apparent magnitude atdiscovery, plotted in Fig. 6, shows a peak at mag r = 23.5 − 24that is consistent with what expected given the detection efficien-cies (see Sect.3).

We found that 57% of the SNe are of type Ia, 19% of typeII, 9% of type IIn and of 15% type Ib/c. We notice that the per-

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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8redshift

0

5

10

15

20

25 Ia

Ibc

II

IIn

Fig. 7. Redshift distribution of the discovered SNe for the different types

centage in different subtypes is quite close to the fraction of SNtypes in magnitude limited samples. For instance the updatedAsiago SN Catalog8 includes 56% Ia, 27% II, 4% IIn and 10%Ib/c (counting only SNe discovered since 2000) with some dif-ference from our sample only for the most uncertain events clas-sified as type IIn. The result is encouraging when consideringthat we did not make any assumption on the fraction of the dif-ferent SN types in our typing procedure. This also implies thatthe relative rates of the different SN types are similar in the localUniverse and at z ∼ 0.5.

At the same time the SN type distribution in our sample isvery different from that derived in a volume limited sample likethat derived for the LOSS survey (Li et al. 2011b) which gavethe following SN type fraction: Ia 24%, Ib/c 19%, II 52 % andIIn 5%. The much higher fraction of SN Ia in our sample is anatural consequence of the high luminosity of SN Ia comparedwith other types that makes it possible to discover SN Ia in amuch larger volume. This also explains the SN redshift distribu-tion shown in Fig. 7. While SN Ia are found to z ∼ 0.8, the red-shift limit for the discovered SN II is only z ∼ 0.4. The relativelyrare but bright type IIn are, on average, discovered at higher red-shifts.

6. The galaxy sample

To relate the occurrence of SN events to their parent stellar pop-ulation, we need to characterise the galaxy population in the sur-vey fields and in the redshift range explored by the SN search. Tothis aim, the extensive multi-wavelength coverage of both COS-MOS and CDFS gives a unique opportunity. In particular theanalysis of deep multi-band surveys of the COSMOS field has al-ready been published (Muzzin et al. 2013), and we could retrievethe required information, such as photometric redshifts, galaxymasses and star formation rates directly from public catalogs.For the CDFS fields we performed instead our own analysis butclosely following the method described by Muzzin et al. (2013).In the following, we describe the detection and characterisationof the galaxies in our search field.

8 http://sngroup.oapd.inaf.it/asnc.html

6.1. COSMOS field

A photometric catalog of the sources in the COSMOS field hasbeen produced by Muzzin et al. (2013), and available troughthe UltraVISTA survey Web site9. The catalog covers an areaof 1.62 deg2 and encompasses the entire 1.15 deg2 area moni-tored by SUDARE. The catalog includes photometry in 30 bandsobtained from: i) optical imaging from Subaru/SuprimeCam(grizBV plus 12 medium/narrow bands IA427 – IA827) andCFHT/MegaCam (u∗) (Taniguchi et al. 2007; Capak et al. 2007),ii) NIR data from VISTA/ VIRCAM (Y JHK bands, McCrackenet al. 2012), iii) UV imaging from GALEX (FUV and NUVchannels, Martin et al. 2005a), iv) MIR/FIR data from Spitzer’sIRAC+MIPS cameras (3.6, 4.5, 5.8, 8.0, 24 and 70, 160 µmchannels from Sanders et al. 2007 and Frayer et al. 2009).

The optical and NIR imaging for COSMOS have compara-ble though not identical PSF widths (FWHMs are in the range0.5′′ − 1.2′′). For an accurate measurement of galaxy colours,Muzzin et al. (2013) performed the PSF homogenisation by de-grading the image quality of all bands to the image quality ofthe band with worst seeing (with seeing of 1′′– 1.2′′). Sourcedetection and photometric measurements were performed us-ing the SExtractor package in dual image mode with the non-degraded K image adopted as the reference for source detection.The flux_auto in all bands was measured with an aperture of2.5 times the Kron radius that includes > 96% of the total flux ofthe galaxy (Kron 1980). Hereafter, the K-band magnitude wascorrected to the total flux by measuring the growth curve ofbright stars out to a radius of 8′′ (depending on the magnitudethis correction ranges between 2%-4%).

The space-based imaging from GALEX, IRAC and MIPShave more complicated PSF shapes and larger FWHM, there-fore photometry for these bands was performed separately (seeSec. 3.5 and 3.6 in Muzzin et al. 2013).

The photometry in all bands is corrected for Galactic dust at-tenuation using dust maps from Schlegel et al. (1998) and usingthe Galactic Extinction Curve of Cardelli et al. (1989). The cor-rections were of the order of 15% in the GALEX bands, 5% inthe optical and < 1% in the NIR and MIR.

Star vs. galaxy separation was performed in the J −K versusu − J color space where there is a clear segregation between thetwo components (Fig. 3 of Muzzin et al. 2013). Sources wereclassified as galaxies if they match the following criteria:

J − K > 0.18 × (u − J) − 0.75 for u − J < 3.0J − K > 0.08 × (u − J) − 0.45 otherwise (2)

The photometric redshifts for the galaxy sample were ob-tained with the EAZY10 code (Brammer et al. 2008). EAZYfits the galaxy SEDs with a linear combinations of templatesand includes optional flux- and redshift-based priors. In addi-tion, EAZY introduces a rest frame template error function to ac-count for wavelength dependent template mismatch. This func-tion gives different weights to different wavelength regions, andensures that the formal redshift uncertainties are realistic.

The template set adopted by Muzzin et al. (2013) includes: i)six templates derived from the PEGASE models (Fioc & Rocca-Volmerange 1999), ii) a red template from the models of Maras-ton (2005), iii) a 1 Gyr old single-burst (Bruzual & Charlot 2003)model to improve the fits for galaxies with post starburst-like

9 http://www.strw.leidenuniv.nl/galaxyevolution/ULTRAVISTA/10 http://www.astro.yale.edu/eazy/

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SEDs and iv) a slightly dust-reddened young population to im-prove the fits for a population of UV-bright galaxies. Muzzinet al. (2013) chose to use the v1.0 template error function, andthe K magnitude prior, and allowed photometric redshift solu-tions in the range 0 < z < 6.

Photometric redshifts are extremely sensitive to errors inphotometric zeropoints. A common procedure to address thisproblem is to refine the zeropoints using a subsample of galax-ies with spectroscopic redshifts (e.g., Ilbert et al. 2006; Brammeret al. 2011). Muzzin et al. (2013) used an iterative code devel-oped for the NMBS survey (see Whitaker et al. 2011) and foundzeropoint offsets of the order of ∼ 0.05 mag for the optical bandsand of 0.1-0.2 mag for the NIR bands.

To remain above the 90% completeness limit and guaran-tee the consistency with the CDFS catalog (see next section) weselected from the full COSMOS catalogue all galaxies with Kband magnitude ≤ 23.5. We further restrict the catalog to the skyarea coverage of our field of view (1.15 deg2) and redshift range0 < z < 1 of interest for the SN search, obtaining a final count of67417 galaxies.

6.2. VOICE-CDFS

Areas of different sizes around the original CDFS field have beenvariously observed at different depths from the X-ray throughthe UV, Optical, IR to the Radio. The 0.5 deg2 Extended CDFS(ECDFS) multi-wavelength dataset has been carefully reducedand band-merged over the years (e.g. Cardamone et al. 2010;Hsu et al. 2014, and reference therein). Conversely, most pub-lic multi-wavelength data over the VOICE-CDFS 4 deg2 areahave been collected recently, and are not available as an homoge-neous database for our study. For our purposes we thus collected,merged and analysed most existing data ourselves.

Available data over the VOICE-CDFS area include the fol-lowing:

– GALEX UV deep imaging (Martin et al. 2005b). TheGALEX photometry is from the GALEX GR6Plus7 data re-lease11.

– SUDARE/VOICE u, g, r, i deep imaging (this work, Vaccariet al. in prep.)

– VISTA Deep Extragalactic Observations (VIDEO, Jarviset al. 2013) Z,Y,J,H,K deep imaging

– SERVS Spitzer Warm 3.6 and 4.5 micron deep imaging(Mauduit et al. 2012)

– SWIRE Spitzer IRAC and MIPS 7-band (3.6, 4.5, 5.8, 8.0,24, 70, 160 micron) imaging (Lonsdale et al. 2003)

While data products are available as public catalog formost of the multi-wavelength surveys listed above, SERVS andSWIRE data were re-extracted and band-merged with all otherdatasets as part of the Spitzer Data Fusion project (Vaccari et al.2010, http://www.mattiavaccari.net/df/).

With the VIDEO survey still in progress, at the moment thesky areas covered by SUDARE and VIDEO do not fully over-lap: restricting our analysis to the overlapping region, for galaxydetection we lack a small portion of our VOICE-CDFS1 andVOICE-CDFS2 (0.14 and 0.05 deg2, respectively) . However,for the estimate of SN rates in the cosmic volume surveyed bySUDARE we use the full area covered by the two fields.

Deep image stacks have been obtained from SU-DARE/VOICE data as described in Sect. 2.1. The VIDEO

11 http://galex.stsci.edu/GR6/

16 18 20 22Ks [ABmag]

100

102

103

104

N(g

al)

[deg−

2]

CDFS

COSMOS

Fig. 8. Distribution of K magnitude for galaxies in CDFS (solid line)and COSMOS (dashed line) catalogues.

0.0 0.2 0.4 0.6 0.8 1.0redshifts

0.05

0.10

0.15

N(g

al)

[%

]VOICE-CDFS

COSMOS

Fig. 9. Redshift distribution (zpeak) for galaxies in CDFS (solid line) andCOSMOS (dashed line) catalogues.

exposures were processed at the Cambridge AstronomicalSurvey Unit (CASU) using the pipeline developed specificallyto the reduction of VIRCAM data, as part of the VISTA DataFlow System (VDFS)12 (Irwin et al. 2004). The stacks producedby CASU were weighted mean combined using SWarp.

CDFS stacks in optical and NIR filters show a very smallvariation in seeing (ranging from 0.8′′to 0.9′′) and we do notneed to perform PSF homogenisation for measuring colours, butonly to resample both VST and VISTA images to the same pixelscale of 0.21′′pixel−1 (for this we used SWarp).

Source detection and photometry for VOICE-CDFS wereperformed with SExtractor in dual image mode with the K bandimage used as reference for source detection.

The photometry was corrected for Galactic extinction corre-sponding to a flux correction of 3% in the optical and < 1% inthe NIR. Then, we separated galaxies from stars using Eq. 2.

12 http://casu.ast.cam.ac.uk/surveys-projects/vista/technical/data-processing

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For all galaxies in the catalog we obtained photometric red-shift using the EAZY code adopting the same parameters andtemplates described in Sect. 6.1. The main difference betweenthe two fields is the number of filters available for the analysis,12 filters for CDFS and 30 for COSMOS. To reduce catastrophicfailures, we do not compute photometric redshifts for the sourcesthat have detection in less than 6 filters (< 5%).

Similar to Muzzin et al. (2013), the magnitude zero-pointswere verified using the iterative procedure developed by Bram-mer (p.c.). The procedure is based on the comparison of pho-tometric to spectroscopic redshifts: systematic deviations aretranslated into zero-point offsets corrections using K as the "an-chor" filter , the photometric scale is adjusted and EAZY re-run.We did not calculate offsets for GALEX and Spitzer bands. Wefound that g, r, i bands require small offsets (≤ 0.05 mag) whilethe u band requires an offset of 0.14 mag, and NIR bands ofabout 0.1 mag.

Also for VOICE-CDFS we selected all galaxies with K bandmagnitude < 23.5 and redshift 0 < z ≤ 1 that results in a finalcatalog of 92324 galaxies for VOICE-CDFS that, allowing alsofor a small overlap of the two pointings, covers 2.05 deg2 .

The distribution of K band magnitudes and photometric red-shifts for the COSMOS and VOICE-CDFS galaxy samples areshown in Fig. 8 and in Fig. 9 respectively.

6.3. Accuracy of photometric redshifts

We explored different methods to assess the quality of photomet-ric redshifts : i) analysing the width of confidence intervals, andquality measurements provided by EAZY, ii) comparing differ-ent redshift estimators, iii) comparing the photometric redshiftswith available spectroscopic redshifts, iv) comparing our esti-mates with photometric redshifts from other groups.

6.3.1. Internal error estimates

EAZY provides multiple estimators of the photometric redshiftsamong which we choose zpeak corresponding to the peak of theredshift probability distribution P(z). As a measure of the uncer-tainty, the code provides 68, 95 and 99% confidence intervalscalculated by integrating the P(z). The confidence intervals are astrong function of the galaxy apparent magnitude and redshift, asshown in Fig.10 for the 68% level. The narrower confidence in-tervals for the COSMOS field with respect to the VOICE-CDFSfield are due to the better sampling of the SED for the galaxiesof the former field.

EAZY provides also a redshift quality parameter, Qz13 that

is intended as a robust estimate of the reliability of the photo-metric redshift (Brammer et al. 2008). Poor fits (Qz > 1) maybe caused by uncertainties in the photometry, poor match of theintrinsic SED from the adopted templates, or degeneracies andnonlinear mapping in the colour-z space. We found good qual-ity photometric redshifts (Qz ≤ 1) for 75% and for 93% of thegalaxies in CDFS and COSMOS field, respectively.

In several cases the P(z) function is multimodal, so that zpeak,that corresponds to the peak of P(z), does not properly reflectthe probability distribution. Wittman (2009) introduced a verysimple alternative estimator that represents the redshift proba-bility distribution, incorporating the redshift uncertainties. This

13 Qz =χ2

Nfilt−3u99−l99

p∆z=0.2where Nfilt is the number of photometric mea-

surements used in the fit, u99 − l99 is the 99% confidence intervals andp∆z=0.2 is the fractional probability that the redshift lies within ±0.2 ofthe nominal value.

20 21 22 23K mag

0.0

0.1

0.2

0.3

0.4

< |

u6

8-l

68

| >

CDFS

COSMOS

0.0 0.2 0.4 0.6 0.8z_peak

0.1

0.2

0.3

< |

u6

8-l

68

| >

CDFS

COSMOS

Fig. 10. The width of the 68% confidence intervals computed from theredshift probability distribution as a function of galaxy magnitude (toppanel) and redshift (bottom panel).

redshift estimator is drawn randomly from the P(z) and denotedwith zMC because it results from a Monte Carlo sampling of thefull P(z). The difference between zpeak and zMC can be used as anindication of the internal uncertainties of photometric redshifts.The difference in the redshift distribution obtained with differentredshift estimators can be seen in Fig. 11.

6.3.2. Comparison with Spectroscopic Redshifts

Spectroscopic redshifts are available for a fairly large numberof galaxies for both our fields. The spectroscopic redshifts for4733 galaxies in the COSMOS field were taken from Muzzinet al. (2013) while for the CDFS field the data for 3362 galax-ies were collected from the literature, from different sources andwith different quality flags. A comparison of photometric andspectroscopic redshifts for these subsample is shown in Fig. 12.

We calculated the normalized, median absolute deviation14,(NMAD) which is less sensitive to outliers compared to the

14 σNMAD = 1.48 × median∣∣∣∣ ∆z−median(∆z)

1+zspec

∣∣∣∣ as in Brammer et al. (2008)where ∆z = (zphot − zspec). The normalization factor of 1.48 ensures thatNMAD of a Gaussian distribution is equal to its standard deviation, andthe subtraction of median(∆z) removes possible systematic offsets

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0.0 0.2 0.4 0.6 0.8 1.0redshifts

0.05

0.10

0.15

0.20

N

(gal)

[%

]

CDFSz_peak

z_mc

0.05

0.10

0.15

0.20 COSMOS

Fig. 11. The distribution of zpeak (solid line) and zMC (dashed line) ofthe galaxies in COSMOS (top panel) and in CDFS catalogues (bottompanel).

standard deviation (Brammer et al. 2008). For CDFS we foundσNMAD = 0.02 which is comparable to that of other surveyswith a similar number of filters, whereas for COSMOS σNMAD =0.005.

Another useful indication of the photometric redshift qualityis the fraction of "catastrophic" redshifts defined as the fractionof galaxies for which

∣∣∣zphot − zspec∣∣∣ /(1+zspec) > 5σNMAD. For the

CDFS field we found a fairly large fraction of catastrophic red-shifts (∼ 14%). After removing these outliers, the rms dispersion∆z/(1+ z) = 0.02. The same analysis for the COSMOS field (seeMuzzin et al. 2013, for details) gives a fraction of 5σ outliersas low as 4% and a very small rms dispersion for the rest of thesample (0.005).

6.3.3. Comparison with zphot from other surveys

The comparison between photometric and spectroscopic red-shifts is biased towards brighter galaxies for which it is easierto observe the spectrum. To analyse the accuracy of our photo-metric redshifts in a wider luminosity range, we compare ourestimates to those obtained by the Multiwavelength Survey byYale-Chile (MUSYC, Cardamone et al. 2010) which covers the∼ 30′ × 30′ “Extended” Chandra Deep Field-South (that is in-cluded in CDFS1) with 18 medium-band filter optical imag-ing from the Subaru telescope, 10 broad-band optical and NIRimaging from the ESO MPG 2.2 m (Garching-Bonn Deep Sur-vey), ESO NTT and the CTIO Blanco telescopes along with 4MIR bands IRAC imaging from Spitzer SIMPLE project. TheMUSYC catalog lists BVR-selected sources with photometricredshifts derived with the EAZY program. Therefore the maindifference is that the MUSYC catalogue makes use of a muchlarger number of filters compared with SUDARE, which signifi-cantly improves the photometric redshift accuracy.

Cross correlating the two catalogs with a search radius of 2′′we found 1830 common galaxies. In Figure 13, we plot the dif-ferences between the zphot estimates as a function of the zMUSYC

phot .We find evidence of a some systematic difference at low red-shifts z < 0.3, with the zphot from SUDARE being higher, butoverall the two catalogs shows a fair agreement with a scatter∆z/(1 + z) = 0.05 and a 5σ outlier fraction of 10%.

0.0

0.2

0.4

0.6

0.8

1.0

z pea

k

0.0 0.2 0.4 0.6 0.8 1.0zspec

-0.2

0.0

0.2

z spec−z p

eak

0.0

0.2

0.4

0.6

0.8

1.0z p

eak

0.0 0.2 0.4 0.6 0.8 1.0zspec

-0.2

0.0

0.2

z spec−z p

eak

Fig. 12. Comparison of photometric vs. spectroscopic redshifts fromCOSMOS (top panel) and CDFS (bottom panel).

7. Computing SN rates

To compute the SN rate we need to introduce the method of thecontrol time (CT, Zwicky 1942). The CT of one observation isdefined as the interval of time during which a SN occurring ata given redshift is expected to remain above the detection limitof the image. The total CT of an observing campaign is prop-erly computed by summing the CT of the individual observa-tions (Cappellaro et al. 1997). Then, the SN rate is computed asthe number of events detected in the survey divided by the totalCT.

The CT depends on the SN luminosity and light curve evo-lution and is therefore different for different SN types. We con-

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0.0

0.2

0.4

0.6

0.8

1.0

z pea

k

0.0 0.2 0.4 0.6 0.8 1.0zMUSYC

-0.2

0.0

0.2

z MU

SY

C−z p

eak

Fig. 13. Comparison of our photometric redshift for CDFS and MUSYCphotometric redshifts.

sidered separately the following main SN types: Ia, Ib/c, II (in-cluding IIP and IIL), IIn and SLSN.

7.1. The control time

To compute the CT we select a template light curve represen-tative of a given SN subtype (SNi), a redshift (z, in the range0 < z < 1), and an extinction value in the range AV = 0 − 2 mag(in the host galaxy rest frame). To take into account the diversityof the photometric evolution for SNe of different types we useda wide collection of light curve templates (listed in Tab. 3). Weconsidered four representative subtypes for thermonuclear SNe(normal, bright, faint and peculiar) , six subtypes for hydrogenrich SNe (IIP, IIP faint, IIL, IIb, IIn, plus peculiars) three sub-types for stripped envelope SNe (Ib, Ic, Ic broad line) along witha template for SLSN. In some cases, we use a few templates forthe same SN subtype to take into account the photometric vari-ance within the class.

Then:

– we define a useful range for the epochs of explosion. In orderto be detectable in our search, a SN needs to explode in theinterval [t0 − 365d, tK], where t0 and tK are respectively theepochs of the first and last observations of the given field.In fact, for the redshift range of interest of our survey, a SNexploded 1 yr earlier than the first observation is far too faintto be detected.

– we compute the expected magnitude, mi, at each epoch ofobservations, ti (with i = 1, 2, ....K, where K is the numberof observations), for a SN that explodes at an epoch x j in-cluded in the time interval defined above. To derive theseestimates, we use the SN template light curve, the properK-corrections, the distance modulus for the selected redshiftand the adopted extinction.

– the detection probability pi(x j) of the simulated event at eachobserving epoch is given by the detection efficiency for theexpected magnitude, εi(mi), estimated as described in Sect.3.

The detection probability for the whole observing campaignis derived as the complement of the probability of not detec-tion at any of the epochs, that is p(x j) = 1−

∏Ki=0 (1 − pi(x j)).

– we simulate a number N of events exploring the possibleepochs of explosion, in the interval [t0 − 365d, tK]. We canthen compute:

CTSNi,EBV (z) = (tK − t0 + 365)

∑Nj=1 p(x j)

N(3)

where t is expressed in Julian Day. The accuracy of the CTcomputation above depends on the sampling for the explo-sion epoch in the defined interval. After some experiments,we found that a sampling of 1d is more than adequate, con-sidering also the contribution of other error sources.

– for the extinction distribution, following Neill et al. (2006)we adopted an half-normal distribution with σE(B−V) = 0.2.We adopt the same distribution for all SN subtypes althoughwe may expect that different SN types, exploding in differ-ent environments, may suffer different amount of extinction.In particular, the distribution of Neill et al. (2006) was de-rived for SN Ia and is likely to underestimate the effect forCC SNe. In Sect. 7.3 we verify (a posteriori) the consistencyof our assumptions about the extinction distribution and esti-mate how its uncertainty propagates in the systematic uncer-tainty of SN rates.

– finally, the CT for each of the main SN types was com-puted by accounting for the subtype distribution and for theadopted extinction distribution (details below):

CTSN(z) =∑SNi

∑EBV

fSNi gEBV CTSNi,EBV (z). (4)

where fSNi is the SN subtype fraction and gEBV the distribu-tion of colour excess E(B−V) (multiple templates for a givensubtype are given equal weight).

In principle, with sufficient statistics and accurate subtypeclassification, the fractional contribution of the different sub-types can be derived from the distribution of detected events.However, at the current stage of the project, the event statistics isnot large enough and we adopted the subtype distribution fromthe literature. In particular, we adopted the fractions of differentSN subtypes obtained by Li et al. (2011b) with the exceptionof the fraction of faint type II SNe that is from Pastorello et al.(2004) while for bright Ic SNe we refer to Podsiadlowski et al.(2004). Our adopted subtype distribution is:

– type Ia: 70% normal, 10% bright 1991T-like, 15% faint1991bg-like and 5% 2002cx-like ;

– type II: 60% IIP, 10% 2005cs-like , 10% 1987A-like, 10%IIL and 10% IIb,

– type Ib/c: 27% Ib, 68% Ic and 5% 1998bw-like,– type IIn: 45% 1998S-like, 45% 2010jl-like, 10% 2005gj-like,

We stress that these subtype distributions are obtained from alocal sample and it is possible, or even expected, that they evolvewith cosmic time. In Sect. 7.3 we estimate the uncertainty im-plied by this assumption.

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0.0 0.2 0.4 0.6 0.8 1.0redshift

101

102

103

contr

ol ti

me [

d]

Ia

II

Ibc

IIn

Fig. 14. Control time as a function of redshift for different SN types (indays per deg2) averaged across the three survey fields.

7.2. SN rate per unit volume

The volumetric SN rates per redshift bins in the range 0 < z < 1is calculated as:

rSN(z) =(1 + z)V(z)

NSN(z)CTSN(z)

. (5)

where NSN(z) is the number of SNe of the given type in the spe-cific redshift bin, CTSN(z) is the control time and the factor (1+z)corrects for time dilation. V(z) is the comoving volume for thegiven redshift bin that is computed as:

V(z) =4π3

Θ

41253

cH0

∫ z2

z1

dz′√ΩM(1 + z′)3 + ΩΛ

3

Mpc3 (6)

where Θ is the search area in deg2 and z is the mid-point of theredshift bin with extremes z1, z2.

7.3. Statistical and systematic errors

We derive the 1-σ lower and upper confidence limits from theevent statistics as in Gehrels (1986). Then, these values are con-verted into confidence limits of SN rates through error propaga-tion of Eq. 5.

There are many sources of systematic errors. To estimateeach specific contribution we performed a number of experi-ments calculating SN rates under different assumptions.

Transient MisclassificationAs described in Sect.4.1, for a fraction of SN candidates

(23%) the SN confirmation remains uncertain. These PSNeare attributed a weight 0.5 in the rate calculation. To obtain anestimate of the impact of this assumption, we compute the ratein the extreme cases assuming for these events a weight 0 and1 respectively. As an error estimate, we take the deviation fromthe reference value of the rates obtained in the two extremecases. It turns out the error is of the order of 10 − 15% (Tab. 6).One concern is that we set arbitrary thresholds for Pχ2 and Npt toattribute the flag of PSN. To test the impact of this assumption,we computed the SN rate adopting different thresholds, namely

10−3 or 10−6 for Pχ2 and 5 or 9 for Npt. We found that in allcases the deviations for the reference value are < 10% (typically∼ 5%).

SN photometric typingFor the errors of on SN typing we adopt the values discussed

in Sect. 4 that is 10% for type Ia, 25% for type II and 40% fortype Ib/c and IIn, independently on redshift. It appears that theerror on SN typing has in general a moderate impact for typeIa, and instead it is one of the dominant sources for SN CC ingeneral.

Subtype distributionThe adopted SN subtype distribution affects the estimate of

SN rates because the subtypes have different light curves hencedifferent control times. We consider an error of 50% for thefraction of the subclasses and, as an estimate of the contributionto the systematic error, we take the range of values of the SNrates obtained with the extreme subtype distribution. This is asignificant source of error, typically 10-20% but with a peak of40% for type IIn SNe.

Detection efficienciesWe performed MonteCarlo simulations assuming that the

value of the detection magnitude limit of each observation hasnormal error distribution with σ = 1.0 mag. We found thatthe frequent monitoring of our survey means that the largeuncertainty in the detection efficiency for each single epochdoes not have a strong impact on the overall uncertainty. Thepropagated error on the rates is ≤ 10%.

Host galaxy extinctionIn our computation we adopt a half-normal distribution ofE(B − V) with σ = 0.2 mag for both type Ia and CC SNe.To estimate the effect of this assumption, the SN rates havebeen recalculated assuming a distribution with σ = 0.1 magand σ = 0.3 mag. We evaluate that the error on the rates is ofthe order of 5 − 10%. The uncertainty is more critical for CCSNe (11%) and for the highest redshift bin of type Ia SNe (14%).

The consistency of the adopted extinction distribution wasverified a-posteriori. We computed estimates of the SN ratesfor a range of σE(B−V) values ranging from 0 to 0.5 mag. Foreach adopted σE(B−V), we computed the expected distributionof extinction of the detected SNe. This is different from theintrinsic distribution because of the bias against the detection ofSNe with high extinction which have a shorter control time. Theexpected extinction distribution is compared with the observeddistribution (Fig. 15) and the best matching σ is determinedusing a Kolmogorov-Smirnov 2-side test. We found a bestmatch for σE(B−V) = 0.25 mag using the full SN sample orσE(B−V) = 0.28 mag using only the type Ia events and excludingprobable SNe (PSN). Given the uncertainties, we considerthat this values are consistent with the adopted distributionfrom Neill et al. (2006). We remind that the adopted E(B − V)distribution was used only for the CT calculation and not for theSN photometric classifier.

Photometric redshiftsWe compare the results obtained using the two alternative

photo-z estimatator zpeak and zmc (c.f. Sect.6.3.1). It turns outthat this is the most significant source of error, especially fortype Ia SNe. A detailed analysis shows that the most importanteffect is for the redshift of SN host galaxies, while the effect on

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0.0 0.1 0.2 0.3 0.4 0.5 0.6E(B-V)

0.0

0.2

0.4

0.6

0.8

1.0

Fig. 15. Predicted (line) vs. observed (bars) cumulative extinction dis-tribution.

Table 6. Relative systematic errors

Ia CC IIn< z > 0.10 0.25 0.45 0.65 0.10 0.25 0.55PSN - 0.04 0.11 0.12 0.26 0.16 0.23SN typing 0.10 0.10 0.10 0.10 0.32 0.31 0.40subtype distr. 0.08 0.10 0.20 0.24 0.07 0.12 0.40detection eff. 0.02 0.05 0.05 0.07 0.02 0.05 0.03extinction 0.04 0.03 0.07 0.14 0.02 0.11 0.05z distribution 0.33 0.21 0.17 0.21 0.09 0.07 0.03all 0.36 0.26 0.31 0.39 0.43 0.39 0.61

the control time of the galaxy population has a smaller impact.

Cosmic varianceThe possible under/over-density of galaxies in the field of

view due to cosmic variance impacts on the SN rate measure-ments. Using the cosmic variance calculator of Trenti & Stiavelli(2008) we found that cosmic variance can add an uncertainty ofthe SN rate of 5-10% but for the low redshift bin the variancecan be as large as 15-20%. We note that the cosmic variancebias is averaged out when rate measurements from different skyfields are analyzed together as for SUDARE.

In Table 6 we report the individual systematic errors alongwith the overall error obtained by their sum in quadrature. Wedo not include the effect of cosmic variance in the error budget,since this is not a measurement error. Rather, this is an uncer-tainty related to the particular galaxy sampling in our survey.

The overall systematic error is typically of the order of 30 −40%, and is larger than the statistical error.

8. SN rates as a function of cosmic time

Our SN rates per unit volume are reported in Tab 7. Columns 1and 2 report the SN type and redshift bin, col. 3 gives the num-ber of SNe (the number of PSNe is in parenthesis), col. 4 the ratemeasurements, cols. 5 and 6 the statistical and systematic errorsrespectively. The redshift bins were chosen to include a signif-icant number of SNe (a minimum number of 10 SNe) with theexception of the nearest redshift bin (0.05 < z < 0.15) , wherewe collected only few SNe.

Table 7. SN rates per unit volume [10−4 yr−1 Mpc−3]. Upper limits werecomputed for a reference value of 3 events. In this case, from Poissonstatistics, the probability of obtaining a null results is ≤ 5%.

SN type zbin SNe rate stat. syst.

Ia

0.05-0.15 3.0 0.55 −0.29 +0.50 ±0.200.15-0.35 12.7 0.39 −0.12 +0.13 ±0.100.35-0.55 23.0 0.52 −0.13 +0.11 ±0.160.55-0.75 17.4 0.69 −0.18 +0.19 ±0.27

CC 0.05-0.15 5.9 1.13 −0.53 +0.62 ±0.490.15-0.35 26.2 1.21 −0.27 +0.27 ±0.47

II 0.15-0.35 13.4 0.69 −0.18 +0.16 ±0.24Ib/c 0.15-0.35 9.3 0.48 −0.17 +0.19 ±0.23

IIn 0.15-0.35 3.5 0.043 −0.026 +0.030 ±0.0260.35-0.75 5.8 0.017 −0.009 +0.009 ±0.010

SLSN 0.35-0.75 − < 0.009

8.1. Core collapse SNe

Fig. 16 shows a comparison of our estimate of the rate of CCSNe with all measurements available in the literature. To obtainthe CC SN rate we cumulated type II, Ib/c and IIn events.

Our results are in good agreement with other measurements.Note that in Fig. 16 we report the value of Melinder et al. (2012)and Dahlen et al. (2012) with no correction for the fraction ofhidden SNe (Mattila et al. 2012). We will return on this pointlater.

Given the short lifetime of their progenitors (< 30 Myr),there is a simple, direct relation between the CC SN and the cur-rent SF rate:

rCC(z) = KCC × ψ(z) (7)

where ψ(z) is the SFR and KCC is the number of stars per unitmass that produce CC SNe, that is:

KCC =

∫ mU,CC

mL,CCφ(m)dm∫ mU

mLmφ(m)dm

(8)

where φ(m) is the initial mass function (IMF), mL and mU arethe extreme limits of the stellar mass range, mL,CC and mU,CC themass range of CC SN progenitors.

Assuming that KCC does not evolve significantly in the red-shift range of interest, the evolution of the CC SN rates withredshift is a direct tracer of the cosmic SF history (SFH). Con-versely, we can use existing estimates of the SFH to computethe expected CC SN rate, assuming a mass range for their pro-genitors. In order to do this consistently one has to use the sameIMF (or KCC) adopted to derive the SFR. Indeed, although inEq.8 Kcc depends on the IMF, the ratio between the cosmic SFRand CC rate does not give a real indication on the IMF, sinceboth quantities actually trace the number of massive stars, whichproduce both UV photons and CC SN events. The formal depen-dence on the IMF of this ratio is introduced by the extrapolationfactor used when deriving the SFH from luminosity measure-ment to convert the number of massive stars formed at the vari-ous redshifts into the total stellar mass formed.

Article number, page 16 of 25

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0.0 0.2 0.4 0.6 0.8 1.0redshift

1.0

10.0

r CC

[10−

4yr−

1M

pc−

3]

Cappellaro etal 1999

Cappellaro etal 2005

Botticella etal 2008

Bazin etal 2009

Li etal 2011b

Graur etal 2011

Melinder etal 2012

Dahlen etal. 2012

Taylor etal. 2014

Graur etal. 2015

SUDARE

Hopkins & Beacom 2006

Madau & Dickinson 2014

Fig. 16. CC SN rate per unit volume. All measurements do not account for the correction for hidden SNe. To obtain the predicted SN rate fromthe measured SFR we adopt 8, 40 M as the lower and upper mass limits for SN CC progenitors and the proper IMF, Salpeter for Madau &Dickinson (2014) and SalA for Hopkins & Beacom (2006). The dashed lines show the predicted SN rate assuming the fraction of hidden SNegiven in (Mattila et al. 2012).

The CC progenitor mass range is still uncertain, both for thelow and upper limit. Stellar evolution models suggest a typicalrange of 9 − 40 M (Heger et al. 2003) for CC SNe, though theupper limit strongly depends on metallicity and other factors,e.g. rotation or binarity. In recent years, for a number of nearbyCC SN it was feasible to search for the progenitor star in archivalpre-explosion images(Smartt 2009, 2015, and reference therein).This allows to obtain an estimate of the masses of their progen-itor stars or, if not detected, of upper limits. By comparing theobserved mass distribution with the IMF, it was argued that theminimum initial mass is 8 ± 1 M. The same analysis also sug-gests a paucity of progenitors of SN II with mass greater than20 M, which would indicate that these stars collapse directly toa black hole, without producing a bright optical transient (Smartt2009). However this result needs to be confirmed and hereafter,following the trend of the literature in the field, we adopt an up-per limit of 40 M.

With a mass range 8 − 40 M for the SN CC progenitorswe obtain a scale factor KCC = 6.7 × 10−3 M−1

for a standardSalpeter IMF or KCC = 8.8 × 10−3 M−1

for a modified SalpeterIMF (SalA), with a slope of -1.3 below 0.5 M (similar to whatadopted in Hopkins & Beacom 2006).

It has been claimed that assuming the 8− 40 M mass range,the comparisons between the SFH from Hopkins & Beacom(2006) (hereafter HB06) and the published measurements ofCC SN rates showed a discrepancy of a factor 2 at all red-shifts (Botticella et al. 2008; Bazin et al. 2009). Horiuchi et al.(2011) argued that this indicated a "supernova rate problem" forwhich they proposed some possible explanations: either manyCC SNe are missed in the optical searches because of heavydust-obscuration or there is a significant fraction of intrinsicallyvery faint (or dark) SNe when, after core collapse, the wholeejecta falls back onto the black hole.

On the other hand, Botticella et al. (2012) found that CC SNrate in a sample of galaxies within 11 Mpc is consistent with thatexpected from the SFR derived from FUV luminosities. Tayloret al. (2014) based on the SDSS-II SN sample estimated that thefraction of missing events is likely of the order of 20%. Gerkeet al. (2014) performed a search for failed SNe monitoring a sam-ple of nearby galaxies (< 10 Mpc). After 4 yr they found onlyone candidate suggesting an upper limit of 40% for the fractionof dark events among CC SNe that, unfortunately is not yet astrong constraint.

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To detect the CC SNe hidden by strong extinction, a fewinfrared SN searches have been performed in local starburstgalaxies (Maiolino et al. 2002; Mannucci et al. 2003; Mattila& Meikle 2001; Miluzio et al. 2013) in some cases exploitingadaptive optics (Cresci et al. 2007; Mattila et al. 2007; Kankareet al. 2008, 2012) to improve the spatial resolution. However,despite the efforts, it was not possible to unveil the hidden SNe.

An alternative approach to estimate the fraction of hiddenSNe was made with the conservative assumption that all SNein the nucleus of luminous and ultra-luminous infrared galaxies(LIRGs and ULIRGs) are lost by optical survey (Mannucci et al.2007). With this approach, Mattila et al. (2012) suggested thatthe fraction of missed SNe increases from the average local valueof ∼ 19% to ∼ 38% at z ∼ 1.2.

The CC SN rate predicted using two different SFH fromHB06 and the recent results of Madau & Dickinson (2014) (here-after MD14) are shown in Fig. 16. The two SFH lead to differ-ent predictions with a discrepancy of about a factor 2. Indeed,the cosmic SFR derived by MD14 is lower than the HB06’s vir-tually at all redshifts. In addition, the MD14 SF rates assume astraight Salpeter IMF, so that the number of massive stars formedin the Universe at all epochs is further diminished, when com-pared to predictions obtained with HB06’s SFH. Both factorsconcur in producing the final result shown in Fig.16. The predic-tions based on the MD14 SFH are in good agreement with thedata. We note that by applying the Mattila et al. correction forhidden SNe (dashed green lines in the figure) improves the fit atlow redshift but gives a worse comparison at high redshift.

The HB06 SFH instead over-predicts the CC rate, if the pro-genitors come from the mass range 8 to 40 M. In this case, cor-recting for hidden SN improves the agreement at high redshift,but still overestimates the CC SN rates at z < 0.4 (dashed blueline).

On the other hand, as we mentioned above, the uncertaintyin the CC progenitor mass range is still significant. Indeed, mostrecent data collection seems too indicate a lower mass limit ashigh as 9 − 10 M (Smartt 2015). If we use this in combinationwith the upper mass limits for CC SN progenitors of ∼ 20 M(e.g. Smartt 2009; Gerke et al. 2014) and also include the cor-rection for hidden SNe, it would result that the SFR severelyunder-predict the CC rate (by over a factor 3 if we refer to theMD14 SFR).

All together, it is fair to say that the both statistic and system-atics errors on SN rate and SFR measurements and the uncertain-ties on the progenitor mass range are too large to invoke a "SNrate problem" and hence to speculate on possible explanations.

One of the goals we aim to achieve with our survey it toobtain measurements of the evolution of specific SN subtype.While the statistics of the present sample is still small, we canhowever obtain some preliminary measurements.

8.1.1. SN Ib/c

We found that at the mean redshift z = 0.25, type Ib/c are 40 ±13% of CC SNe. This compares very well with the estimatesof Li et al. (2011a) for the local Universe, measuring a fractionof Ib/c that ranges from 46 ± 17% in early spiral galaxies to20 ± 5% in late spirals, with an average value of 33 ± 9%. Thephysical reason of the difference in CC SN population in earlyand late spirals is not understood, though it is possibly related toa metallicity effect (Li et al. 2011a).

Given the limited statistics of our sample, we can only con-clude that there is no evidence for evolution with redshift of theIb/c fraction.

8.2. SN IIn

Our estimate of the rate of type IIn SNe is uncertain for tworeasons: SNe IIn are rare and the event statistics is very poor.In addition, the variety in luminosity and light curve evolutionthat, in some cases, mimic those of other SNe (e.g. SN IIL orSLSN), makes the photometric classification very uncertain (cf.Sect. 7.3). However, due to the intrinsically bright and slowlyevolving luminosity we could detect type IIn SNe in a redshfitrange comparable to that of SN Ia.

In the 0.15-0.35 redshift bin, we estimated that type IIn are4±3% of all CC SNe. This number is consistent with the 6±2%value measured in the local Universe (Li et al. 2011a).

On the other hand, the apparent decrease of the rate at higherredshift (a factor ∼ 2.5 in the redshift bin 0.35-0.75 comparedwith the nearest bin) appears at odd considering that the overallCC SN rate in the same redshift interval increases by about thesame factor.

Either there is a strong evolution of the type IIn rate withredshift or, in our search, we are missing (or mis-classifying)over 2/3 of the distant type IIn. Both explanations are difficult toaccept: we will need to verify this result at the end of our surveywith better statistics and, possibly, improved template list.

8.3. Superluminous SNe

Super-luminous supernovae (SLSNe) that radiate more than1044 erg s−1 at their peak luminosity (about 100 times more lu-minous than usual Type Ia and CC SNe) have recently been dis-covered in faint galaxies typically at high redshifts. The originof these events is still unclear: the host environment and energet-ics suggest massive stellar explosions but their power source isstill a matter of debate. In fact, several sub-classes have been in-troduced possibly related to different explosions scenarios (Gal-Yam 2012).

We did not detect any SLSN in our surveyed volume up toz = 0.75. However the null result for SLSN can provide in-teresting constraints on the rates of these objects. From sim-ple Poisson statistics we find that the probability of obtaininga null result is 5% when the expected values is 3.0. Thereforeone should expect that the rate of SLSNe is no higher than9 × 10−7yr−1Mpc−3 at a mean redshift z ∼ 0.5. This firm up-per limit is consistent with the rate estimated by Quimby et al.(2013) of 2.0+1.4

−0.9 × 10−7 yr−1Mpc−3 at a mean redshift z = 0.16that is 1 SLSN for each 500 CC SNe.

We note that between z = 0.15 and z = 0.5 the CC ratemeasurements increases by almost a factor 3. Our upper limitdoes not preclude a similar increase for the SLSN rate; clearlywe need to obtain a more significative result that will becomefeasible when our survey is completed.

8.4. SNe Ia

Our measurements of the rate of SN Ia are shown in Fig. 17along with all those available from the literature15.

Our results appear in agreement with other measurementswithin the statistical errors. It may be noticed, however, that our

15 We do not plot original measurements that have been later revised orsuperseded as follow: Madgwick et al. (2003) by Graur & Maoz (2013),Poznanski et al. (2007b) by Graur et al. (2011), Dahlen et al. (2004);Kuznetsova et al. (2008) by Dahlen et al. (2008), (Barris & Tonry 2006)by Rodney & Tonry (2010), Neill et al. (2006, 2007) by Perrett et al.(2012)

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EC: Sudare. I.

Table 8. Average of SN Ia rate measurements per redshift bin (units10−4 yr−1 Mpc−3).

zbin < z > rIa σ N0.00-0.15 0.05 0.25 0.05 60.15-0.35 0.25 0.29 0.07 70.35-0.55 0.45 0.44 0.11 90.55-0.75 0.65 0.58 0.14 80.75-1.00 0.84 0.64 0.20 111.00-1.50 1.16 0.87 0.22 71.50-2.00 1.64 0.63 0.22 5

2

4<z>=0.25

<rIa>=0.29±0.07

2

4<z>=0.45

<rIa>=0.44±0.11

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4SN rate

2

4<z>=0.65

<rIa>=0.58±0.15

Fig. 18. Histogram of published estimates of the SN Ia rate in the threeredshift bins of our measurements. All values are scaled to the meanredshift of the bin assuming a linear evolution of the rate with redshift(see text). In red we show our measurements. The black lines are theGaussian curve whose mean and variance are computed from the dataand reported in each panel’s legend. The averages and dispersions werecomputed by weighting the individual measurements with the inverseof their statistical errors.

estimates seem on the high side compared with the bulk of pub-lished measurements.

In Fig. 18 we plot the histogram of the measurements for thethree bins of redshifts corresponding to our measurements. Theeffect of the rate evolution within each of these redshift bins hasbeen removed by scaling the measurements to the mean redshiftof the bin assuming that the rates scales as rIa ∝ 0.6 × z, that, asa first order approximation, fits the rate evolution up to redshift∼ 1 (the exact slope of the relation may be slightly different butit is not crucial for the comparison we are performing here).

It appears from the Figure that for each bin the distribution ofmeasurements (our own included) are consistent with a normaldistribution and the dispersion is well understood consideringthe statistical and systematic errors affecting the measurements.

Given that, in the following we will use average values as thebest estimates of the SN Ia rate for the comparison with models.The average rates per redshift bin are reported in Tab. 8, wherecol. 1 gives the redshift bin, col. 2 the average redshift, col. 3and 4 the average rate and dispersion and col. 5 the number ofmeasurements per bin. Notice that for redshift z > 0.75 in thecomputation of the average rate we did not correct the individualmeasurements for the possible rate evolution inside the bin.

Fig. 19. Distribution functions of the delay times selected for our the-oretical predictions for the Single Degenerate (green), and Double De-generate models (blue and red, see text for more details). The thin linesshow the cumulative fraction of events as a function of time

In Fig. 20 we compare the average SN Ia rate measurementswith the expected evolution for different progenitor scenariospredicted by Greggio (2005).

Models in Greggio (2005) assume that SNIa progenitors areclose binary systems which attain explosion upon reaching theChandrasekhar mass either because of mass accretion from acompanion star (single degenerate, SD) or of merging with an-other WD (double degenerate, DD). The delay between the birthof the binary system and its final explosion ranges from ∼ 40Myr to the Hubble time, so that at each epoch the SN events in agalaxy are the result of the contributions of all past stellar gener-ations. Following Greggio (2005), the expected SN Ia rate at thetime t is:

rIa(t) = KIa

∫ min(t,τx)

τi

fIa(τ)ψ(t − τ)dτ (9)

where KIa is the number of SN Ia progenitors per unit massof the stellar generation, fIa(τ) is the distribution function of thedelay times and ψ(t − τ) is the star formation rate at the epocht − τ. The integration is extended over the full range of the delaytime τ in the range τi and min(t, τx), with τi and τx being the min-imum and maximum possible delay times for a given progenitorscenario. According to stellar evolution, fIa(τ) is a decreasingfunction of the delay time, with a slope which depends on de-tails of the scenario leading to the SN explosion. One can thenuse Eq. 9 to constrain the progenitor’s model using the trend ofthe SNIa rate with cosmic time, after specifying the cosmic SFH.In the following we adopt the MD14 SFH and assume that KIadoes not vary with cosmic time.

We select three DTD models, plotted in Fig. 19, and test theirpredictions for the cosmic SNIa rate. The models include a sin-gle degenerate realisation (SD), and two flavours of the doubledegenerates, either with a close (DDC) or wide (DDW) binaryseparation predicting a steep and a mildly decreasing distribu-tion of the delay times, respectively (see Greggio 2005, 2010, for

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0.0 0.5 1.0 1.5 2.0 2.5redshift

0.1

1.0

r Ia

[10−

4yr−

1M

pc−

3]

Cappellaro etal 1999

Hardin etal 2000

Pain etal 2002

Tonry etal 2003

Blanc etal 2004

Botticella etal 2008

Horesh etal 2008

Dahlen etal 2008

Dilday etal 2010

Li etal 2011b

Rodney & Tonry 2010

Graur etal 2011

Melinder etal 2012

Barbary etal 2012

Perrett etal. 2012

Graur & Maoz 2013

Graur etal. 2014

Okumura etal. 2014

Rodney etal. 2014

SUDARE

Fig. 17. Our estimates of the SN Ia rate at z = 0.25, 0.45, 0.65 are compared with the other values from literature. The rate of Cappellaro et al.(1999); Hardin et al. (2000); Madgwick et al. (2003); Blanc et al. (2004) were given per unit luminosity. They are converted in rate per unit volumeusing the following relation of the luminosity density as a function of redshift : jB(z) = (1.03 + 1.76 z)× 108 LB

Mpc−3 (Botticella et al. 2008). Themeasurements of Perrett et al. (2012) are scaled up by 15% to account for the fact that they not include the faint SN 1991bg-like events.

more details). The selected models correspond to a very differenttime evolution following a burst of star formation. For the DDC,SD and DDW models, 50% of the explosions occur within thefirst 0.45, 1 and 1.6 Gyr respectively, while the fraction of eventswithin 500 Myr is 0.55, 0.3 and 0.18 of the total. The late epochdeclines are also different, the rate scaling as t−1.3 and t−0.8 forthe DDC and DDW models respectively.

Fig. 20 shows the predicted rates as a function of redshiftfor each of the three models, having assumed the Madau andDickinson cosmic SFH. The best fit of the models with obser-vations was derived by least square minimisation weighting themeasurement by their σ and gives KIa = 7.5 × 10−4 M−1

for SDand DDW and 8.5 × 10−4 M−1

for DDC.

The smallest residuals in the whole redshift range is ob-tained for the SD model (rms= 0.0028). The DDC model (rms=0.0088) gives an excellent fit up to redshift z ∼ 1.2 but predicts arelatively mild decline at higher redshift (still not in conflict withthe observations). Instead, the DDW model (rms= 0.012) showsan overall shallow evolution with respect to the observations.Our conclusion is that since the dispersion of the rate measure-ments is comparable to the scatter of theoretical tracks we cannot

discriminate between SD and DD models, tough for the DD sce-narios model with close binary separation seems favoured.

We remark that the impact of adopting different cosmicSFHs, MD14 or HB06, on the predicted rate is negligible, whileit is more relevant for the estimate of constant KIa, that is thenumber of stars which end up as a SNIa per unit mass of theparent stellar population. In particular, fitting the observationswith HB06 SFH requires KIa = 5.9, 5.7, 6.9 ×10−4 M−1

for SD,DDW and DDC respectively, values which are ∼ 20% smallerthan those obtained when using the MD14 SFH. The number ofpotential progenitors per unit stellar mass depends on the IMFand, assuming from to 8 M range of SN Ia progenitors, it is0.021 for a plain Salpeter IMF and 0.028 for a SalA IMF. Starsin the selected mass range that should end up as SN Ia t oaccountfor the observed rates are 4% and 2% using the MD14 and HB06SFH, respectively. These fractions are close to the lower edge ofthe range reported in Maoz and Mannucci (2012). While theyremain large with respect to most theoretical predictions frombinary population synthesis codes, they still represent a minorfraction of all potential progenitors.

In any case, we conclude that there is no need to invoke ad-hoc delay time distributions, unrelated to the standard expecta-

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EC: Sudare. I.

0.0 0.5 1.0 1.5 2.0redshift

0.1

1.0

r Ia

[10−

4yr−

1M

pc−

3]

SD

DDC

DDW

Fig. 20. Average values of the SN Ia rates per unit volume as a func-tion of redshift. The still unique measurement at z > 2 of Rodney et al.(2014) is also plotted. For the derivation of the SNIa rate model evolu-tion we adopted the SFH from Madau & Dickinson (2014).

tions from stellar evolution theory. This confirms earlier conclu-sions (e.g. Förster et al. 2006; Blanc & Greggio 2008; Greggioet al. 2008).

In a forthcoming paper (PII) we will use a detailed charac-terization of the properties of the galaxy sample in order to in-vestigate the dependence of SN rates on galaxy parameters, andto obtain additional constraints on the SN progenitor scenarios.

9. Conclusions

We presented the preliminary results of a new SN search, SU-DARE, that was designed to measure SN rates in the redshiftrange 0 < z < 1. This paper describes the survey strategy, theselection and confirmation of candidates, the construction of thegalaxy catalog and the rate estimates based on the first two yearsof the survey.

As search fields we selected two of the best studied extra-galactic fields, namely CDFS and COSMOS for which a wealthof multi-band coverage is available. Our own data, the syn-ergy with the VOICE project, the complementary data from theVIDEO survey, along with public data from the literature al-lowed us to obtain a multi-band photometric catalog for thegalaxies with magnitudes K ≤ 23.5 that was exploited to esti-mate the photometric redshift using the EAZY code (Brammeret al. 2008).

We discovered 117 SNe, of which 27 were assigned aweight= 0.5 due to a poor template match or to a low numberof detections. Most of the SNe are classified of type Ia (57%).For the core collapse, 44% are type II, 22% type IIn and 34%type Ib/c.

With this SN sample and an accurate measurement of the de-tection efficiency of our search, we computed the rate of SNeper unit volume. For the CC SNe our measurements are inexcellent agreement with previous results and fully consistentwith the predictions from the cosmic SFH of Madau & Dickin-son (2014), assuming a standard mass range for the progenitors(8 < M < 40 M). Therefore, previous claims of a significantdisagreement between SFH and CC SN rates are not confirmed.This conclusion relies on the revision of the cosmic SFH be-

cause our rate estimates are consistent withother measurementsfrom literature.

For the SN Ia, our measurements are consistent with liter-ature values within the errors. We conclude that the dispersionof SN Ia rate estimates and the marginal differences for the evo-lution with cosmic time of the volumetric SN rate does not al-low us to discriminate between SD and DD progenitor scenarios.However, with respect to the three tested models(SD, DDC andDDW from Greggio 2010), the SD gives a better fit on the wholeredshift range whereas the DDC appears to perfectly match thesteep rise of the rate up to redshift 1.2. The DDW model that cor-responds to a wide binary separation and a relatively flat delaytime distribution appears disfavoured.

As a first attempt at searching for evolution of SN diversity,we found no evidence of evolution of the SN Ib/c fraction. Thefraction of type IIn SNe detected in the 0.15 < z < .35 redshiftbin is consistent with the measurements for the local Universe.The rate in the higher redshift bin is formally significantly lower.Whether this is evidence for some evolution or a bias in our sur-vey needs to be verified with more data.Acknowledgements. We thank the referee, Steve Rodney, for useful commentsand suggestions. We also thank G. Brammer for helping with galaxy photomet-ric redshifts.We acknowledge the support of grant ASI n.I/023/12/0 "Attivitá relative allafase B2/C per la missione Euclid", MIUR PRIN 2010-2011 "The dark Universeand the cosmic evolution of baryons: from current surveys to Euclid" and PRIN-INAF "Galaxy Evolution With The VLT Survey Telescope (VST)" (PI A. Grado).G.P. acknowledge the support by Proyecto Regular FONDECYT 1140352 andby the Ministry of Economy, Development, and Tourism’s Millennium ScienceInitiative through grant IC12009, awarded to The Millennium Institute of As-trophysics, MAS. MV acknowledges support from the Square Kilometre Ar-ray South Africa project, the South African National Research Foundation andDepartment of Science and Technology (DST/CON 0134/2014), the EuropeanCommission Research Executive Agency (FP7-SPACE-2013-1 GA 607254) andthe Italian Ministry for Foreign Affairs and International Cooperation (PGR GAZA14GR02).

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Article number, page 23 of 25

A&A proofs: manuscript no. sudare_2015_A_v7Ta

ble

4.co

ntin

ued.

desi

gnat

ion

RA

DE

Cz(

host

)er

rso

urce

type

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mpl

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)A

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]χ

2 nN

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2

cdfs

2_r_

2011

1020

_201

2012

5_8A

3:29

:55.

2-2

7:58

:04.

4-

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0.61

1991

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390.

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355

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2)1.

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r_20

1110

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141A

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1990

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856

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1110

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1110

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2_r_

2011

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5574

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727

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2_r_

2011

1115

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2012

3_3A

3:30

:06.

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7:19

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40.

49[.3

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3]ph

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1990

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880

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2)1.

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1990

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890

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4)5E

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5588

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831

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2_r_

2011

1118

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2012

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phot

Ia1.

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431

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2_r_

2011

1118

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2012

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44:1

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.78]

phot

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5588

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830

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2_r_

2011

1118

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2012

5_5A

3:31

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1990

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924

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1203

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phot

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2_r_

2011

1203

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824

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2_r_

2012

0114

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2000

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946

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56:1

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2_r_

2012

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2_r_

2012

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.61]

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5594

80.

59

(7)

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cdfs

2_r_

2011

1025

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2012

3_14

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29:0

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49:0

7.8

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phot

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9719

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0.72

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5583

51.

023

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2_r_

2011

1025

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2012

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phot

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523

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2_r_

2011

1028

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2012

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4A3:

30:4

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59:1

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spec

IIn

0.86

2005

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2_r_

2011

1028

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2012

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1.00

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cdfs

2_r_

2012

0118

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79

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2012

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06:5

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2_r_

2011

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2012

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12:5

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45.1

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1992

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phot

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5590

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535

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Article number, page 24 of 25

EC: Sudare. I.Ta

ble

4.co

ntin

ued.

desi

gnat

ion

RA

DE

Cz(

host

)er

rso

urce

type

P typ

ete

mpl

ate

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)A

V∆

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]χ

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72:

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.39]

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836

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1991

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355

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2004

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355

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0120

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0120

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phot

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5599

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625

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1990

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0120

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2011

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os_r

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1121

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1205

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phot

Ia1.

0019

99ee

0.70

-0.3

-0.3

5592

22.

231

(15)

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P=

Prob

able

SNe

(see

text

)a-S

N20

12ez

;b-S

N20

12fa

IAU

C32

36c

-SN

2012

fn;d

-SN

2012

fp;e

-SN

2012

fq;

f-S

N20

12fo

IAU

C32

74g

-SN

2012

gu;h

-SN

2012

gs;i

-SN

2012

gv;

j-SN

2012

gtIA

UC

3311

*H

ostg

alax

yre

dshi

ftob

tain

edin

this

prog

ram

**SN

type

revi

sed:

base

don

lyon

spec

trum

was

clas

sifie

das

Ic)

Article number, page 25 of 25