AMICO:optimised detection of galaxy clusters in photometric surveys

ArXiv:1705.03029

Matteo Maturi

Center for Astronomy, Heidelberg University

With: F. Bellagamba, M. Roncarelli, L. Moscardini

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The manifestations of cosmology

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Use all what we know about them

N-Body simulations Gravitational lensing(s)

X-ray emission

Galaxy overdensity

SZ

filaments

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Intermediate maps

ICM – baryons Galaxies

X-rays SZ Optical LensingICM – baryons Dark matter

Data

Filter(s) ->

Estimator ->

The Idea? Multi-band Optimal Matched Filter

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Data Model

Noise covariance

Estimator

Filter

Multi-band optimal matched filter

← We assume a template for each band

← Derive statistics from data

← Derive the filter constrain minimization- minimum variance- unbiased estimator

← Apply the filter (filtered map)

Formalism

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Multi-band optimal matched filter ← The n-dimensional space in which we work

← We have discrete data

← Likelihood to be a cluster

← Galaxy-Clusters probability association

When using galaxies only...

(Ra,Dec)(m

1, m

2,..., c

1, c

2, ...)

(z)

← The variance of the estimate- minimum variance- unbiased estimator

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The filter ‘face’

A. Gelsin PhD thesis

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Optical photometry: ra, dec, m1, m

2, …, P(z)

Step 1: Running the filter, amplitude map

z

One redshift slicesEuclid Challenge 4

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Step 2: cleaning, de-blending

Procedure: (1) run the filter over the whole data sample (2) identify the detection with the largest S/N (3) associate membership to galaxies (bayesian) (4) remove the detection → (2) ….

(6) estimate new template and noise properties → (1) ...

Simulations: Merson et al. (2013): Millennium + “galform”

Input: - ra, dec, m

1, m

2, …, P(z)

Products: - cluster x,y,z,S/N, grav. potential proxy - probabilistic association of galaxies - clusters removed map (field galaxies)

Probability association of galaxies to clusters

Iterative removal of clusters

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Completeness

Pur

ity

- Simulations: Merson et al. (2013): Millennium + “galform”- M>1013.5 M_sun, 0<z<inf

Performances: purity vs completeness

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Including weak gravitational lensing...

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Example on CFHTLens

Weak gravitational lensing alone

Optical data: not only photometry → ellipticity

Maturi et al. (2005)

Pace et al. (2007)

Pace et al. (2007)

N-Body: Hennawi & Spergel (2005)

NFW

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Photometry + weak lensing (preliminary)

- larger S/N- less blending (angular)

Lensing Optical Combined

5 1014 1015

1014

5 1013

z=0.1, 17 arcmin-1

z=0.9, 1.8 arcmin-1

Laila Linke

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Bonus material!

EasyCritics I: Efficient identification of strongly-lensing groups and clusters

S. Stapelberg, M. Carrasco, M. Maturi, G. Seidel, T. Erben

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1- Ellipticals: power-law profile

2- Smooth halo by convolving for over-dense regions-------

Total deflection field of the SL cluster

Only 4 parameters:

Galaxies: q , Kq , Haloe: σ , K

gal

3- Calibrate with known arcs

EasyCriticsS. Stephelberg, M, Carrasco & M. Maturi in prep.Maturi et al 2014

- LTM: Use elliptical galaxies only- Split in redshift bins (lens planes)- Create lensing potential- Identify the critical curves on the sky- look for strong lensing features (+ color + arcfinder, G. Seidel)

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Examples

5

Einstein radious

Redshift slicing

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AMICO:optimised detection of galaxy clusters in photometric surveys

ArXiv:1705.03029

Conclusions:

- Catalog of galaxies: x, y, P(z), m1, m2, …

- Multi-band Optimal Matched filter

- Amplitude map

- Cleaning – deblending

- Catalog of galaxies

- Probability association of galaxies to clusters

- G. lensing...

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Split signal and noise

Maturi (2016)

Image denoising with EMPCA

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The clean galaxies in SkyLens

(M. Meneghetti)

XDF galaxies

Simulations for strong/weak lensing

Maturi (2016)