The Evolving Relation between Star Formation Rates and Stellar Mass
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Transcript of The Evolving Relation between Star Formation Rates and Stellar Mass
UV Visi NI FIRMIR
Russell JohnstonCollaborators: Mattia Vaccari, Matt Jarvis, Mat Smith, Matt Prescott, Elodie Giovannoli
The evolving relation between SFR and M* in the VIDEO survey since z = 3
I. A bit of background
II. Creating our star-forming sample
III. Results part 1 - the star-forming main sequence
IV. Results part 2 - simulations and implications
A Bit of Background
• How do galaxies evolve?
• What are the physical processes driving that evolution?
a
To probe star formation histories of galaxies, the key components are:
M! yr-1
Measure of the present
activity of the galaxy
M! !
Measure of the past activity of the
galaxy.
Galaxy spectra is the sum of the different components:
!STARS !
GAS !
DUST !
We need access to the full stellar emission to determine
these quantities
young old dust
SFR M★
star formation ratestellar mass
Estimating Star Formation Rates
Estimating Star Formation Rates
➡ Dust in galaxies absorbs UV and optical photons !➡ Which is then re-emitted at infrared wavelengths
Visible Infrared
Dust
Log 1
0[λF
λ(t)
] (er
g s-
1 M
!-1
)Wavelength (μm)
• UV - emission dominated by young massive short-lived star.
• UV+IR - Account for dust attenuation in the UV.
• Nebular emission lines - , ,
• Radio continuum emission and stacking.
• SED Modelling e.g. CIGALE and MAGPHYS
H↵ O[II] O[III]
Estimating Star Formation Rates
The SFR-Mass “Main Sequence”
Noeske et al. 2007
Daddi et al. 2007Elbaz et al. 2007
DEEP2, K-band imaging and Spitzer MIPS 24 µm
GOODS, SDSS, Spitzer 3.6, 4.8 µm MIPS 24 µm UV, radio, mid and far IR
An Emerging Picture
➡ SF galaxies follow tight SFR-Mass relation.
➡ SFR increases with Mass as a Power-law.
➡ Intrinsic scatter
0.2 . �MS . 0.35
➡ Strong evolution in the n o r m a l i s a t i o n w i t h redshift
➡ Measurements of slope vary wildly in literature
0.2 < ↵ < 1.2
SFR / M↵⇤
The VIDEO Survey VISTA Deep Extragalactic Observations
( Jarvis et al. 2013 )
VIDEO
Spitzer SWIRE
CFHTLS-D1
UKIDSS-UDS
!➡ 12 deg^2 !➡ (NIR): Z, Y, J, H, K
s
➡ Visible: ugriz (CFHTLS)
➡ zphot
< 4.0
➡ zphot
obtained from !
LePhare
➡ SERVS (Spitzer Extragalactic Representative Volume Survey, Mauduit et al. 2012)
IRAC 1 - 3.6 µm IRAC 2 - 4.5 µm
Joint selection
then matched to
➡ SWIRE (Spitzer Wide-Area Extragalactic, Lonsdale et al. 2003)
IRAC 1 - 5.8 µm IRAC 2 - 8.0 µm
➡ HerMES (Herschel Multi-tiered Extragalactic Survey, Olivier et al. 2012)
SPIRE 250, 350 and 500 µm
MIPS 24, 70 and 160 µm
Matching to Multi-wavelength data
First Things First
• SFR indicator
• Mass Completeness
• Cosmic Variance
• Star-forming selection criteria
• Calibration
Code Investigating GALaxy Emission (CIGALE) (Burgarella et al. 2005; Noll et al. 2009b)
CIGALE INPUT• Photometric broad-bands
• Star Formation History
• Dust Attenuation
• IR Library
CIGALE OUTPUT• SFR
• M*
• LDUST
• .... etc...
Combines UV-optical stellar SED with dust emission in IR
to conserve energy balance between dust absorbed emission
and its re-emission in IR
Wavelength (µm)SPIRE
HerschelSpitzer
ZYJHK
VIDEO
ugriz
CFHTLS
MIPSIRAC
exponentially decreasing tau models
Kroupa IMF
PEGASE
FIR part of the spectrum
Dale & Helou (2002) !64 templates
6 AGN models
Code Investigating GALaxy Emission (CIGALE) (Burgarella et al. 2005; Noll et al. 2009b)
0.0 0.5 1.0 1.5 2.0 2.5 3.0redshift (zph)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
N
VIDEO + CFHTLS + SERVS
0.0 0.5 1.0 1.5 2.0 2.5 3.0redshift (zph)
0
50
100
150
200
250
300
350
400
450MIPS (24 µm)IRAC (5.8 µm)SPIRE (250 µm)
Matching to Multi-wavelength data
�0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
log(
SFR
2)[M
�y�
1 ]
8.5
9.0
9.5
10.0
10.5
11.0
11.5
log(
M⇤ 2)
[M�
]
�1.0 �0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5log(SFR1)[M� y�1]
�1.0
�0.5
0.0
0.5
1.0
log(
SFR
1/S
FR2)
8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0log(M⇤
1) [M�]
�1.0
�0.5
0.0
0.5
1.0
log(
M⇤ 1/
M⇤ 2)
0.3
0.6
0.9
1.2
1.5
1.8
2.1
2.4
2.7
reds
hift
MIPS detected sources : 0.1 < z 3.0
Sensitivity of CIGALE to wavelength coverage
(Opt
ical
+ N
IR +
MIR
)
(Optical+NIR+MIR+IR+FIR)
Mass Completeness Limits • Joint selection in Ks with SERVS 3.6 & 4.5 µm
log10(Mlim) = log10(M⇤) + 0.4(Ks �K lims )
- [Ilbert et al. (2013)]
0.5 1.0 1.5 2.0 2.5 3.0redshift
0.0
0.1
0.2
0.3
0.4
0.5
cosm
icva
rianc
e(�
v)
Dark Matter8.5 < log(M⇤) 9.09.0 < log(M⇤) 9.59.5 < log(M⇤) 10.010.0 < log(M⇤) 10.510.5 < log(M⇤) 11.011.0 < log(M⇤) 11.5
Cosmic Variance • VIDEO currently only covers 1 sq. deg
• Uncertainty in observed number density of galaxies arising from the underlying large-scale density fluctuations.
Moster et al. (2011)‘GETCV’
• Determined using predictions from CDM and theory and galaxy bias
Selecting Star Forming Galaxies • Common to perform rest-frame colour selection e.g. UVJ, U-B, BzK, u-g
• or sigma-clip
• mixture: NUV-r and r-J Ilbert et al. 2014
Schreiber et al. 2015
(Magnelli et al. 2014, Santini et al. 2009)
(e.g. Daddi et al. 2007, Whitaker et al. 2014, Rodighiero et al. 2011,)
• Avoids selecting the bluest galaxies
Colour cut (u-r)
0.5 1.0 1.5 2.0 2.5 3.0100
600
1100
1600
2100
2600
N
0.5 1.0 1.5 2.0 2.5 3.0
U � R
8
9
10
11
log(
M⇤ /
M�
)
Selecting Star Forming Galaxies
1.0 1.1 1.2 1.3 1.4 1.5100
600
1100
1600
2100
N
1.0 1.1 1.2 1.3 1.4 1.5
D4000
8
9
10
11
log(
M⇤ /
M�
)
D4000 break
Selecting Star Forming Galaxies
D4000<1.3
• An output from CIGALE. • Related to the age of a stellar population. • low D4000 index = younger SP • high D4000 index = older SP
Aratio of the average flux per frequency unit of the
wavelength ranges 4000–4100 Å and 3850–3950 Å Balogh et al. 1999
Calibrating the Main Sequence
• Comparing your results to other works is very tricky! Wavelength coverage/selection
SFR estimation (L-SFR relation)
Initial mass function (IMF)
Stellar population synthesis models (SPS)
Star forming galaxy selection
Star formation histories (SFH) [difficult to correct/calibrate] Extinction Metallicities Adopted cosmology Dust attenuation Photometric redshifts Incompleteness
!!
(see Speagle et al. 2014)
|{z} affects normalisation
affects slope
log(SFR) = ↵ log(M⇤) + �
log(SFR) = A1 +A2 log(M⇤) + A3[log(M⇤)]2
(e.g. Noeske et al. 2007, Daddi et al. 2007, Elbaz et al. 2007, Santini et al. 2009, Heinis et al. 2014)
SFR = ↵
✓M⇤
1011 M�
◆�
log[SFR(z)] = ↵(z)[log(M⇤)� 10.5] + �(z)
↵(z) = ↵1 + ↵2z
�(z) = �1 + �2z + �3z2
where,
(e.g. Magnelli et al 2014, Whitaker et al 2014)
(e.g. Whitaker et al 2012)
Modelling the Data
9.0 9.5 10.0 10.5 11.0 11.5
�1
0
1
2
3
This work
Dunne et al. (2009)Noeske et al. (2007a)Oliver et al. (2010)Santini et al. (2009)
Rodighiero et al. (2010)Whitaker et al. (2012)Schreiber et al. (2014)Magnelli et al. (2014)
0.10 < z 0.80Ngal = 10357 (7487)
9.5 10.0 10.5 11.0 11.5
This work
Dunne et al. (2009)Elbaz et al. (2007)Rodighiero et al. (2010)Whitaker et al. (2012)Heinis et al. (2014)Schreiber et al. (2014)Magnelli et al. (2014)
0.80 < z 1.20Ngal = 10976 (7746)
10.5 11.0 11.5
This work
Daddi et al. (2007)Dunne et al. (2009)Pannella et al. (2009)Reddy et al. (2012)Rodighiero et al. (2011)
Santini et al. (2009)Zahid et al. (2012)Rodighiero et al. (2010)Whitaker et al. (2012)Schreiber et al. (2014)Magnelli et al. (2014)
1.90 < z 2.10Ngal = 2956 (1520)
log(M⇤ [M�])
log(
SFR
[M�
y�1 ]
)
z~0.45 z~1 z~2
The Star Forming Main Sequence
↵ = 0.7± 0.01 ↵ = 0.7± 0.01 ↵ = 0.83± 0.02
0.0 0.5 1.0 1.5 2.0 2.5 3.0
redshift
�0.2
0.0
0.2
0.4
0.6
0.8
1.0
↵
This work (D4000)Daddi et al. (2007)Santini et al. (2009)Rodighiero et al. (2011)Noeske et al. (2007a)
Elbaz et al. (2007)Dunne et al. (2009)Pannella et al. (2009)Rodighiero et al. (2010)Oliver et al. (2010)
Zahid et al. (2012)Reddy et al. (2012)Whitaker et al. (2012)Whitaker et al. (2014)Whitaker et al. (2014)
The Star Forming Main Sequence
What about photo-z uncertainties?
• Common approach to bin in redshift
• Lephare outputs full z-PDF
• Can we propagate this in our modelling?
0.2
0.4
0.6
0.8
1.0pr
obab
ility
�0.5
0.0
0.5
1.0
0.2 0.4 0.6 0.8 1.0
0.2 0.3 0.4 0.5 0.6 0.7redshift
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
log(
M⇤
[M�
])
0.2 0.4 0.6 0.8 1.0
log(
SFR
[M�
y�1 ]
)
probability
z-PDF from Lephare
run CIGALE in series of z-steps for each galaxy
Weigh the resulting SFR and M* distributions by the z-PDF probability
log10[SFR(z)] = ↵(z)[log10(M⇤)� 10.5] + �(z),
↵(z) = ↵1 + ↵2z, and
�(z) = �1 + �2z + �3z2
median constraints - DOES NOT INCLUDE zPDF uncertainties
‘all data’ constraints - INCLUDES zPDF uncertainties
(Whitaker et al 2012)
log10[SFR(z)] = ↵(z)[log10(M⇤)� 10.5] + �(z),
↵(z) = ↵1 + ↵2z, and
�(z) = �1 + �2z + �3z2
OUR median constraints
Whitaker et al. (2012) (medians)
High mass SFR turn-off?
Whitaker et al. (2014)stacking in MIPS 24µm
Tasca et al. (2014) [VUDS]
Magnelli et al. (2014) [PACS, HerMES]
9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4log(M⇤ [M�])
0.5
1.0
1.5
2.0
log(
SFR
[M�
y�1 ]
)
D4000 < 1.30D4000 < 1.35Whitaker et al. (2014)
1.00 < z 1.50
0.0 0.5 1.0 1.5 2.0 2.5 3.0redshift
0.0
0.2
0.4
0.6
0.8
1.0
↵
This work (D4000)This work (u � r)
Star-Forming Selection Effects
�10.5
�9.5
�8.5
�7.5
9.85 < M⇤ < 10.15sSFR / (1 + z)3.56±0.01
Feulner et al. (2005)Noeske et al. (2007a)Dunne et al. (2009)
Whitaker et al. (2012)Salmi et al. (2012)
Ilbert et al. (2013)Heinis et al. (2014)
�10.5
�9.5
�8.5
�7.5
10.15 < M⇤ < 10.45sSFR / (1 + z)3.09±0.03
Zheng et al. (2007)Daddi et al. (2007)Kajisawa et al. (2009)
Rodighiero et al. (2010)Karim et al. (2011)Ilbert et al. (2013)
Zwart et al. (2014)Tasca et al. (2014)Schreiber et al. (2014)
�10.5
�9.5
�8.5
�7.5
10.45 < M⇤ < 10.65sSFR / (1 + z)2.60±0.04
Noeske et al. (2007a)Dunne et al. (2009)Karim et al. (2011)
Whitaker et al. (2012)Salmi et al. (2012)
Ilbert et al. (2013)Heinis et al. (2014)
�10.5
�9.5
�8.5
�7.5
10.65 < M⇤ < 10.85sSFR / (1 + z)2.15±0.04
Zheng et al. (2007)Daddi et al. (2007)Kajisawa et al. (2009)Rodighiero et al. (2010)
Ilbert et al. (2013)Zwart et al. (2014)Schreiber et al. (2014)
0.0 0.5 1.0 1.5 2.0 2.5 3.0�10.5
�9.5
�8.5
�7.5
10.85 < M⇤ < 11.15sSFR / (1 + z)2.13±0.06
Feulner et al. (2005)Karim et al. (2011)Whitaker et al. (2012)Heinis et al. (2014)
redshift
log(
sSFR
[y�
1 ])
• At between we find , consistent to Tasca et al. (2014)
log10(sSFR) = log10(SFR)� log10(M⇤)
• Mass dependent evolution out to z<1.4 , similar to that of Ilbert et al. (2014)
• General flattening off beyond
• We model this by
sSFR / (1 + z)�
Evolution of the Specific Star Formation Rate
0.4 < z < 2.46
z > 2
M⇤ ⇠ 10.5
� = 2.60± 0.04
Hydrodynamical: Scaling relations:
➡Horizon - Dubois et al. (2014)
➡Ilustris - Sparre et al. (2014)
➡Davé et al (2013)➡Mitra et al. (2014)
- Equilibrium Model-
What Can Simulations Tell Us?
Star formation gas cooling and heating feedback from stellar winds, supernovae and AGN
analytical - constrained to observed data
Describes motion of gas into and out of galaxies - baryon cycle. 8 free parameters
➡Behroozi et al. (2013)
- HOD-stellar mass-halo mass scaling relation 15 free parameters
9 10 11
0
1
2
3This workIllustris, Sparre et al. (2014)Mitra et al. (2014)Horizon, Dubois et al. (2014)Behroozi et al. (2013)Dave et al. (2013)
z = 1
9 10 11
z = 2
9 10 11
0
1
2
3This work
Horizon � (with cut)
Horizon � (no cut)
9 10 11log(M⇤ [M�])
log(
SFR
[M�
y�1 ]
)
�10.5
�9.5
�8.5
�7.5
9.85 < M⇤ < 10.15
Behroozi et al. (2013)Mitra et al. (2014)Illustris � Sparre et al. (2014)Horizon � Dubois et al. (2014)
�10.5
�9.5
�8.5
�7.5
10.15 < M⇤ < 10.45
Mitra et al. (2014)Horizon � Dubois et al. (2014)
�10.5
�9.5
�8.5
�7.5
10.45 < M⇤ < 10.65
Behroozi et al. (2013)Mitra et al. (2014)Illustris � Sparre et al. (2014)Horizon � Dubois et al. (2014)
�10.5
�9.5
�8.5
�7.5
10.65 < M⇤ < 10.85
Mitra et al. (2014)Horizon � Dubois et al. (2014)
0.0 0.5 1.0 1.5 2.0 2.5 3.0�10.5
�9.5
�8.5
�7.5
10.85 < M⇤ < 11.15
Behroozi et al. (2013)Illustris � Sparre et al. (2014)Mitra et al. (2014)Horizon � Dubois et al. (2014)
redshift
log(
sSFR
[y�
1 ])
• Hydro show lower normalisation by factor of 2-6 between 0.5<z<~3.0
• Good agreement with scaling relation approaches.
Implications
• Discrepancy between hydro/SAMs and observations well known
• Oversimplified gas accretion modelling?
• Systematic offsets in gas cooling rates?
• Insufficient sub-grid models that control star formation
and stellar feedback?
(Daddi et al. 2007; Elbaz et al. 2007; Santini et al. 2009; Damen et al. 2009b; Davé et al. 2013; Sparre et al. 2014; Genel et al. 2014; Tasca et al. 2014)
• Currently this remains an unresolved issue:
Implications
• Equilibrium model [Mitra et al. (2014)] does not explicitly model: • halos, • cooling, • mergers or • a disk star formation law
• Parameterises the motion of gas into and out of galaxies
• Is continual smooth accretion regulated by continual outflows a key driver in the overall growth of SFGs?