Gas inflow and outflow ofstar-forming galaxies around cosmic noonas revealed by chemical evolution
Nao Fukagawa(SOKENDAI/Tohoku University)
T. Kodama(Tohoku University), et al.

Galaxy Workshop@Kashiwa
6 June 2019

Outline
1. Background
Dependence of gas flows on properties of galaxies(e.g. environment)
2. Assumptions and model
3. Comparison between observational data and model
to constrain rates of inflow and outflow ofindividual star forming galaxies at z~1.4→Derived ranges of IFR and OFR are broad
4. Simulation of the constraint on rates
using mock galaxies→IFR and OFR can be constrained if we have precise
data, but there might be some caveats
5. Summary

BackgroundWhy measure gas inflow and outflow?
Quantifying gas flows and studying the dependence on
properties of galaxies provide insights into the evolution
Environmental dependence is still unknown
General field
Gas accretes onto galaxies
stochastically throughfilaments
Interstellar gas is expelled
by supernovae and/or AGN

BackgroundWhy measure gas inflow and outflow?
Quantifying gas flows and studying the dependence on
properties of galaxies provide insights into the evolution
Environmental dependence is still unknown
High number density→Formation of common halo
→Inefficient inflow

Gas removal through tidal effects
and/or ram pressure stripping↩︎︎

Fall back of expelled gas
due to pressure of IGM

↪︎↪︎

↩︎︎

Dense regions(cluster of galaxies)

These effects related to outflow
might be more prominent
for lower mass galaxies

BackgroundWhy measure gas inflow and outflow?
Quantifying gas flows and studying the dependence on
properties of galaxies provide insights into the evolution

↪︎↩︎︎

↪︎

The difference of gas flows
might be reflected to theproperties of galaxies↩︎︎Environmental dependence is still unknown

Uncover mass- & environmental dependence ofgas flows from the viewpoint of chemical evolution

Assumptions and model
mG,HAccretion ofprimordial gas

mG
Star formation rate :(t) =· mG (t)formation epoch :tform (Gyr)Inflow rate :F(t) = a · mG,H (t)a : accretion
efficiency (/Gyr)mS: star formation
efficiency (/Gyr)(determined by data)

IMF: Chabrier(03)Abundance of element i
SNII : M ≥ 10Msun (Nomoto+2013)

Outflow of gas &heavy elements
Outflow rate :O(t) =·(t): mass-loading factor

Time variation ofgas mass fraction &metallicity※ Z = 12 + log(O/H)

Assumptions and model
Evolution of gas & element idmG
dt =(t) + EG (t) + F(t)O(t)

gas→stars stars→gas inflow outflow

d(mG Xi )=dt(t)Xi (t) + Ei (t)mG,HmGmS

O(t)Xi (t)Inflow rate :F(t) = a · mG,H (t)SFE(ν)=0.2(/Gyr),
ACE(a)=0.4(/Gyr),
η=1.0time

Outflow rate :Star formation
rate : (t) = · mG (t)O(t) =·(t)MObservables

Model parameters

gas-phase metallicity
gas mass fraction
specific SFR
stellar population age

accretion efficiency
mass loading factor(star formation efficiency)(formation epoch)g= Mg +M

inflow rate &outflow rate

Constraints of rates of inflow & outflow

1.6
1.6
1.4

Z/Zsolar
Z/Z

1.2
1.2
1.0

0.8
0.8
0.6
0.4
0.4

SXDS5_28019

0.2

0.0
0.0
1.0
1.0

Seko+2016

0.8
0.8

0.6
0.6

0.4
0.40.2
0.2

0.0
0.0

mass fraction
gas gasmassfraction

Models are extracted as thepredictions are consistent withdata within 1σ errorη= OFR/SFR
η=OFR/SFR

1.8(z~1.4, Seko et al. 2016)

mass-loading factor :-1
accretion
efficiency(Gyr)accretion efficiency (/Gyr)Data : star-forming
main sequence galaxies
in general field

Inflow rate : F = a · mg,hOutflow rate : O = ·parameters
12.0
12.0
10.0

age=0.50Gyr

SXDS5_28019
age=0.5Gyr

8.0
8.0
6.0
4.0
4.0
2.0

0.0
0.02.02.01.5 1.6 1.7 1.8age=0.50Gyr
1.9 2.0 2.1
star formation efficiency (Gyr-1)
1.5
1.5

1.0
1.0

0.5
0.5
0.0
0.0
1.5
1.5 1.6 1.7
1.7 1.8 1.9
1.9 2.0 2.1
2.1
-1
star
formation
efficiency(/Gyr)starformation efficiency(Gyr)Constraints of rates of inflow & outflow

-1

1.8

(z~1.4, Seko et al. 2016)

1.6
1.6
1.4

Z/Zsolar
Z/Z

1.2
1.2
1.0

0.8
0.8
0.6
0.4
0.4

SXDS5_28019

0.2

0.0
0.0
1.0
1.0normalized OFR (Gyr )Data : star-forming
main sequence galaxies in general field

Seko+2016

0.8
0.8

0.6
0.6

0.4
0.4

0.2
0.2

mass fraction
gas gasmassfraction

0.0
0.0

101

10010-1

10-2

10-3

Inflow rate : F = a · mg,hOutflow rate : O = ·mass-loading factor :●: model prediction
age=0.50Gyr10-3 10-2 10-1 100 101
normalized IFR (Gyr-1)

Ranges of IFR&OFR are broad due to uncertainty about
the data & degeneracy of model parameters
To see the mass & environmental dependence,
the rates have to be constrainedConstraints of rates of inflow & outflow

-1

1.8

(z~1.4, Seko et al. 2016)

1.6
1.6
1.4

Z/Zsolar
Z/Z

1.2
1.2
1.0

0.8
0.8
0.6
0.4
0.4

SXDS5_28019

0.2

0.0
0.0
1.0
1.0

normalized OFR (Gyr )Data : star-forming
main sequence galaxies in general field

Seko+2016

0.8
0.8

0.6
0.6

0.4
0.4

0.2
0.2

mass fraction
gas gasmass
fraction

0.0
0.010

110010-110-2

10-3

Inflow rate : F = a · mg,hOutflow rate : O = ·mass-loading factor :●: model prediction
age=0.50Gyr

10-3 10-2 10-1 100 101
normalized IFR (Gyr-1)

Can we well constrain IFR and OFR with this method?
How is the precision of the data related to model parameters?

Constraint of IFR&OFR with mock data
Generatemock galaxies

Deriveprobability functions

1.4

Mock galaxies are generated
based on model predictions

1.2
1.2

Z/Z
Z/Zsolar

1.0

B

0.8
0.8
0.6

Investigate
ranges of IFR&OFR

Fractional error is assigned togas fraction, metallicity and sSFR

A

0.4
0.4
0.2
mock galaxy
0.0
0.0
1.0
0.8
0.6
0.4
0.2 0.0
0.0
1.0
0.8
0.6
0.4
0.2
mass fraction
fractiongasgasmass

Fractional error= Δμ/μ, ΔZ/Z, ΔsSFR/sSFR

age(Gyr)μZ/ZsunsSFR(/Gyr) ACE(/Gyr)

mockA

0.500

0.675

0.434

3.533

mockB

1.000

0.475

0.665

1.536

η

SFE(/Gyr)IFR(/Gyr)

OFR(/Gyr)

1.585

1.259

1.700

0.960

0.566

1.259

1.000

1.700

0.628

0.404

Constraint of IFR&OFR with mock data
Generatemock galaxies

2000

probability
probability↑high1500

Deriveprobability functions

Investigate
ranges of IFR&OFR

Assume Gaussian error for
metallicity, gas fraction, sSFR

error:10%
age:0.2dex

Afractional error=10%

Derive probability functionsof IFR&OFR(uncertainty about age=0.2dex)

1000

500

0.00
-3.0
-3.0 -2.0-2.0 -1.0
-1.0

0.0
0.0

1.0
1.0

OFR/M* (/Gyr)loglogOFR/M* (/Gyr)

2.0
2.0

Examine the cases ofdifferent size of fractional error

Constraint of IFR&OFR with mock dataResults of galaxy A

log IFR/M* (Gyr )log IFR/M* (/Gyr)
-1

1.5
1.5

Deriveprobability functions

age:0.2dex

1
1.0

0.5
0.5

00.0

-0.5
-0.5

IFR/M*(0.50Gyr)
IFR/M*,median16, 84percentiles
16, 84percentiles
00

Investigate
ranges of IFR&OFR#Similar trend for galaxy B

0.1
0.2
0.3 0.4
0.4 0.5
0.5
0.1
0.2
0.3
fractional error
fractional error

1.5
1.5

log OFR/M -1(/Gyr)*)log OFR/M* (GyrGenerate
mock galaxies

age:0.2dex

1
1.0
0.5
0.500.0-0.5-0.5
OFR/M*(0.50Gyr)
OFR/M16,*,median84percentiles
16, 84percentiles-1.0-100.1
0.2 0.3
0.3 0.4
0.4 0.5
0.5
00.1
0.2
fractional error
fractional error

IFR and OFR can be reasonably constrainedeven with degeneracy of modelConstraint of IFR&OFR with mock data
This method might work well
if metallicity of a galaxy is not too highat a given gas fraction

1.4

Smaller mass loading factor,
higher metallicity at a given gas fraction

η=01.2
1.2

Z/ZsolarZ/Z

1.0

η=1

0.8
0.8
0.6

η=2

0.4
0.4
0.2

0.0
0.0
1.0
1.0

0.8
0.8

0.6
0.6

0.4
0.4

gas mass fraction

0.20.2gas mass fraction

0.0
0.0

→If an observed galaxy locates above
model track of no gas flow,
the rates are not derived
Adding assumptions require
more observables to constrain
parameters
Measurement of properties also
involve uncertainty(e.g. line ratios, conversion factor)

Constraint of IFR&OFR with mock dataLogstellarpopulation
population age(yr)(yr)logstellarageThis method might work well
if galaxies are assumed to be young(Yabe et al.2012)

9.5
9.0
9.0
8.5
8.0
8.0
7.5

To explain sSFR,galaxies are assumed to beyounger than 1-Gyr-old→These galaxies started to formstars around z~2
These galaxies are not on the main
sequence at z>2.5

7.0
7.0

6.5
9.0
9.0

9.5 10.0
10.0 10.5 11.0
11.0 11.5
Log M)* (Msolar
log stellarmass(Msolar)

The assumption might be extreme
and inconsistent with otherobservational facts

Summary
What we do :We attempt to quantify inflow and outflow of star-forming
galaxies in general field at z~1.4 using chemical evolution
model.
What we find :The ranges of rates of inflow and outflow can be constrained
if uncertainty about observational data is small.
This method might work well if metallicity of a galaxy is
not so high at a given gas fraction.
Galaxies have to be assumed to be young.

Constraints of rates of inflow & outflow
Data : individual star-forming
main-sequence galaxies
1.8(z~1.4, Seko et al. 2016)

mass-loading factor :Data can be explained if
ages are different
5.0
5.0

1.6
1.6

sSFR
sSFR (/Gyr)(/Gyr)

Z/Zsolar
Z/Z

tauSF=0.0(Gyr) ● DatatauSF=1.0(Gyr)(Seko+16,Yabe+12)
tauSF=3.5(Gyr)Seko+2016

4.0
4.0

1.4
1.2
1.2
1.0

0.8
0.8
0.6
0.4
0.4

3.0
3.0
2.0
2.0

3.5GyrYounger

1.0
1.0

0.2

0.0
0.0
1.0
0.8
1.0 0.8

Old

Seko+2016
0.6
0.6

0.4
0.4

Inflow rate : F = a · mg,hOutflow rate : O = ·0.20.20.00.0

mass fraction
gas gasmassfraction

Predicted sSFRs are much
lower than observed value
Time variation is notsensitive to parameters(e.g., Peng&Maiolino 2014)

0.0
0.0
0.0 2.0 4.0
4.0 6.0 8.0
8.0 10.012.0
12.0 14.0
0.0
timeage(Gyr)(Gyr)cosmicWe treat galactic age as aparameter
Determined range of age iswithin those by SED fitting(Yabe+2012)