Quantifying the interplay between star formation and stellar mass is a crucial component to understanding the build up of galaxies over cosmic time. There have been many investigations of this relationship, using both observations and simulations, with the aim of shedding light on how it connects with the underlying physical processes governing galaxy evolution. In this talk I will present recent work where we have examined the star formation rate (SFR) - stellar mass (M∗) relation of star-forming galaxies in the XMM-LSS field to z ∼ 3.0 using the near-infrared data from the VISTA Deep Extragalactic Observations (VIDEO) survey. Combining VIDEO with broad-band photometry, we have used the SED fitting algorithm CIGALE to derive SFRs and M∗ and have adapted it to account for the full photometric redshift PDF uncertainty. We have also compared our results to a range of simulations where I will show that the analytical scaling relation approaches, that invoke an equilibrium model, a good fit to our data. Within a simplified framework, such a model does not include the modelling of e.g. halos, cooling, or galaxy mergers, suggesting that a continual smooth accretion regulated by continual outflows may be a key driver in the overall growth of SFGs.

1. UV Visi NI FIRMIR
Russell Johnston
Collaborators: MattiaVaccari, Matt Jarvis, Mat Smith, Matt Prescott, Elodie Giovannoli
The evolving relation between SFR and M* in the VIDEO
survey since z = 3

2. 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

3. A Bit of Background
• How do galaxies evolve?
• What are the physical processes driving that evolution?

4. 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

5. • UV - emission dominated by young massive short-lived star.
Estimating Star Formation Rates

6. Estimating Star Formation Rates
➡ Dust in galaxies absorbs UV and optical photons
!
➡ Which is then re-emitted at infrared wavelengths
Visible Infrared
Dust
Log10[λFλ(t)](ergs-1M!-1)
Wavelength (μm)

7. • 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

8. Multi-wavelength Observations
UV Visibl
e
NI
R
FIRMIR

9. 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
nor malisation with
redshift
➡ Measurements of slope
vary wildly in literature
0.2 < ↵ < 1.2
SFR / M↵
⇤

10. CREATING OUR STAR FORMING SAMPLEII.

11. 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, Ks
➡ Visible: ugriz (CFHTLS)
➡ zphot
< 4.0
➡ zphot
obtained from
!
LePhare

12. ➡ 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

13. FirstThings First
• SFR indicator
• Mass Completeness
• CosmicVariance
• Star-forming selection criteria
• Calibration

14. 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

15. 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)

16. 0.0 0.5 1.0 1.5 2.0 2.5 3.0
redshift (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 3.0
redshift (zph)
0
50
100
150
200
250
300
350
400
450
MIPS (24 µm)
IRAC (5.8 µm)
SPIRE (250 µm)
Matching to Multi-wavelength data

17. 0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
log(SFR2)[My1
]
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 3.5
log(SFR1)[M y 1
]
1.0
0.5
0.0
0.5
1.0
log(SFR1/SFR2)
8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0
log(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
redshift
MIPS detected sources : 0.1 < z  3.0
Sensitivity of CIGALE to wavelength coverage
(Optical+NIR+MIR)
(Optical+NIR+MIR+IR+FIR)

18. Mass Completeness Limits
• Joint selection in Ks with SERVS 3.6 & 4.5 µm
log10(Mlim) = log10(M⇤) + 0.4(Ks Klim
s ) - [Ilbert et al. (2013)]

19. 0.5 1.0 1.5 2.0 2.5 3.0
redshift
0.0
0.1
0.2
0.3
0.4
0.5
cosmicvariance(v)
Dark Matter
8.5 < log(M⇤)  9.0
9.0 < log(M⇤)  9.5
9.5 < log(M⇤)  10.0
10.0 < log(M⇤)  10.5
10.5 < log(M⇤)  11.0
11.0 < log(M⇤)  11.5
CosmicVariance
• 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

20. 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

21. Colour cut
(u-r)
0.5 1.0 1.5 2.0 2.5 3.0
100
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

22. 1.0 1.1 1.2 1.3 1.4 1.5
100
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
˚A
ratio of the average flux per frequency unit of the
wavelength ranges 4000–4100 Å and 3850–3950 Å
Balogh et al. 1999

23. 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

24. III. RESULTS PART 1
THE STAR-FORMING MAIN SEQUENCE

25. The Star Forming Main Sequence

26. 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

27. 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  0.80
Ngal = 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  1.20
Ngal = 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  2.10
Ngal = 2956 (1520)
log(M⇤
[M ])
log(SFR[My1
])
z~0.45 z~1 z~2
The Star Forming Main Sequence
↵ = 0.7 ± 0.01 ↵ = 0.7 ± 0.01 ↵ = 0.83 ± 0.02

28. 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

29. What about photo-z uncertainties?
• Common approach to bin in redshift
• Lephare outputs full z-PDF
• Can we propagate this in our modelling?

30. 0.2
0.4
0.6
0.8
1.0
probability
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 0.7
redshift
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[My1
])
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

31. 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)

32. 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)

33. High mass SFR turn-off?
Whitaker et al. (2014)
stacking in MIPS 24µm
Tasca et al. (2014) [VUDS]
Magnelli et al. (2014)
[PACS, HerMES]

34. 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4
log(M⇤
[M ])
0.5
1.0
1.5
2.0
log(SFR[My1
])
D4000 < 1.30
D4000 < 1.35
Whitaker et al. (2014)
1.00 < z  1.50
0.0 0.5 1.0 1.5 2.0 2.5 3.0
redshift
0.0
0.2
0.4
0.6
0.8
1.0
↵
This work (D4000)
This work (u r)
Star-Forming Selection Effects

35. 10.5
9.5
8.5
7.5
9.85 < M⇤ < 10.15
sSFR / (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⇤ < 10.45
sSFR / (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⇤ < 10.65
sSFR / (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⇤ < 10.85
sSFR / (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⇤ < 11.15
sSFR / (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[y1
])
• 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

36. IV. RESULTS PART II
SIMULATIONS AND IMPLICATIONS
!

37. 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 SimulationsTell 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

38. 9 10 11
0
1
2
3
This work
Illustris, Sparre et al. (2014)
Mitra et al. (2014)
Horizon, Dubois et al. (2014)
Behroozi et al. (2013)
Dav´e et al. (2013)
z = 1
9 10 11
z = 2
9 10 11
0
1
2
3
This work
Horizon (with cut)
Horizon (no cut)
9 10 11
log(M⇤
[M ])
log(SFR[My1
])

39. 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[y1
])
• Hydro show lower
normalisation by factor
of 2-6 between
0.5<z<~3.0
• Good agreement with
scaling relation
approaches.

40. 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:

41. 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?