the sfr of mass-selected galaxies out to z
TRANSCRIPT
THE SFR OF MASS-SELECTED GALAXIES OUT TO Z<5 WITH VLA-COSMOS 3GHZ LARGE PROJECT SARAH LESLIE (MPIA) EVA SCHINNERER +JVLA-COSMOS TEAM
@GalacticSKL
SFRD
Madau & Dickinson 2014
Build up of galaxy stellar mass
¨ >66% of stars formed in a galaxy on MS. ¨ Star forming galaxies lie on MS. disp~0.3dex
Schreiber et al. 2015
Flattening of high-mass slope at low z. Increasing contribution of bulge? (e.g. Abramson+2014) SFR method (e.g. Bisigello+2017)???
Continuously rising sSFR
COSMOS
5σ depth
COSMOS is a deep, wide area (2 deg2), multi-wavelength survey aimed at measuring the evolution of galaxies. COSMOS2015. Laigle et al. 2016.
Davidzon +2017
http://cosmos.astro.caltech.edu/
VLA-COSMOS 3GHz Large Project
¨ VLA 3GHz continuum survey over full 2deg2 COSMOS field (Smolcic+2017a)
¨ ~2.3µJy rms, 0.75” resolution ¨ 10 830 radio sources
identified at >5σ ¨ Data now public! ¨ Papers
¤ Smolcic (counterparts) ¤ Delhaize (FIR/radio) ¤ Novak (SF luminosity functions) ¤ Delvecchio (AGN & hosts) ¤ Smolcic (AGN luminosity
function) ¨ here: Eric, Jacinta, Daniel
Advantages of radio continuum
¨ Advantages ¤ No dust attenuation ¤ High angular resolution ¤ No blending issues
¨ However, ¤ Do we really understand it as a SFR tracer … ¤ Can only probe high-mass/high-sfr with direct
detections
à Stacking
Parent sample and binning scheme
¨ COSMOS2015: Laigle+ 2016 for zd<2.5 & Davidzon+ 2017 for zd>2.5.
¨ SF galaxies NUV-r/r-J
Ks selected z<2.5 3.6µm selected for z>2.5
a mass selected sample
Stacking
¨ Create cutouts at each optical position in each bin. ¨ Median combine, clean the stacks, fit 2D Gaussian ¨ Converted to SFR using evolving IR/Radio “q” relation
of Delhaize et al. 2017
Results: Main Sequence
log(M* /[M¤])
Log(
SFR
/[M
¤ y
r-1 ])
8.5 9.0 9.5 10.0 10.5 11.0 11.5log(M⇤)
�1.0
�0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log(
SFR
)
Speagle+14
SF galaxies0.2< z <0.50.5< z <0.80.8< z <1.11.1< z <1.51.5< z <2.02.0< z <2.52.5< z <3.03.0< z <3.53.5< z <4.04.0< z <6.0
Results: sSFR vs M*
log(
sSFR
/[G
yr-1
])
log(M* /[M¤]) 9.0 9.5 10.0 10.5 11.0 11.5
log(M⇤)
�2.0
�1.5
�1.0
�0.5
0.0
0.5
1.0lo
g(sS
FR)
SF galaxies
0.2< z <0.50.5< z <0.80.8< z <1.11.1< z <1.51.5< z <2.0
2.0< z <2.52.5< z <3.03.0< z <3.53.5< z <4.04.0< z <6.0
Is there a maximum sSFR?
Results: Maturity
See Scoville et al. 2013
τSF =M*/SFR a mass doubling time….
Maturity= τSF/τcosm …normalized by the age of the Universe ETG have maturity ~10. LTG have maturity <2
0 1 2 3 4 5z
0.0
0.5
1.0
1.5
2.0
2.5
Mat
urity
t
SF/t
cos
SF galaxies9.0< log(M⇤) <9.49.4< log(M⇤) <9.79.7< log(M⇤) <9.99.9< log(M⇤) <10.110.1< log(M⇤) <10.3
10.3< log(M⇤) <10.510.5< log(M⇤) <10.710.7< log(M⇤) <10.910.9< log(M⇤) <11.6
more mature: most stars formed in the past
Results: sSFR vs z
Schreiber+2015
log(
sSFR
/[G
yr-1
])
0 1 2 3 4 5z
�2.0
�1.5
�1.0
�0.5
0.0
0.5
1.0
log(
sSFR
)
SF galaxies
Bethermin+15Sargent+14Schreiber+15K+11, <log(M⇤)>=10.0K+11, <log(M⇤)>=10.4K+11, <log(M⇤)>=10.8K+11, <log(M⇤)>=11.2
Tasca+159.0<log(M⇤)<9.49.4<log(M⇤)<9.79.7<log(M⇤)<9.99.9<log(M⇤)<10.110.1<log(M⇤)<10.310.3<log(M⇤)<10.510.5<log(M⇤)<10.710.7<log(M⇤)<10.910.9<log(M⇤)<11.6
Next Steps
¨ Convolve with stellar mass functions Davidzon et al. 2017
Karim et al. 2011
Further Steps
¨ Local density: z<3
¤ Filaments z<1
¨ Bayesian analysis: Model
for flux distribution expected and then use MCMC. ¤ Detections and upper limits
Sargent et al. 2014
Image credit: Clotilde Laigle
Summary
¨ SFR from stacking 3GHz for galaxies in COSMOS
¨ Flattening/turn-over in MS at high-M*, z<1.5 ¤ Due to more mature galaxies
¨ More variation in galaxy sSFR/maturity at low-z. ¨ Flattening of sSFR with z ¨ Further work includes
¤ Finalize fluxes ¤ SFD, characteristic stellar mass, environment dependence. ¤ Bayesian approach.
Sarah Leslie (MPIA) @GalacticSKL