ESTEC_Oct_2015

Modelling and interpreting the
SEDs of galaxies in the era of
JWST
Jacopo Chevallard
Postdoctoral Research Fellow @ ESA-ESTEC

J. Chevallard - Modelling and interpreting the SEDs of galaxies in the era of JWST
“Exploring the Universe with JWST” - ESA/ESTEC - 13th October 2015
Collaborators
• P. Ferruit
• G. Giardino
• S. Charlot
• J. Gutkin (PhD)
• A. Vidal (PhD)
• A. Feltre
• E. Curtis-Lake
• M. Hirschmann
• A. Wofford
• D. Stenning
• J. Blaizot
• L. Michel-Dansac
• P. Coelho
• M. Franx
• C. Pacifici
NEOGAL

Outline
1) JWST, early galaxies and cosmic reionization
2) Modelling the SEDs of (high-z) galaxies
3) BANGS: a new analysis framework for galaxy SEDs
4) Broad-band SED fitting with BANGS
5) Simulating JWST/NIRSpec observations

The James Webb Space Telescope
‣ 6.5 m primary mirror
‣ 7x HST collecting area
‣ imaging + spectroscopy in
0.6-28 micron range
FGS/NIRISS
NIRcam
NIRSpec
MIRI
★ wide range of spectroscopic
capabilities
★ low to medium resolution
★ MOS, IFU and single-slit

The James Webb Space Telescope
‣ 6.5 m primary mirror
‣ 7x HST collecting area
‣ imaging + spectroscopy in
0.6-28 micron range
FGS/NIRISS
NIRcam
NIRSpec
MIRI
★ wide range of spectroscopic
capabilities
★ low to medium resolution
★ MOS, IFU and single-slit
“Imaging is Astronomy - Spectroscopy is Astrophysics”
(P. Jakobsen)
JWST: “game changer” for spectroscopic
characterization of distant galaxies

Early galaxies and cosmic reionization
• UV LF at high-z and faint mag
• Early chemical enrichment
from nebular lines
• Dust content
• Stellar masses and SFH
• AGN-SF connection
• AGN and stellar feedback
Early galaxies Cosmic reionization
• Prod. rate of H-ioniz. photons
from stellar pop. analyses
• LyC fesc from UV slope +
Balmer lines (need SFH too)
• LyC fesc from Lyα profiles
• Lyα emitters LF evolution
(and clustering)
• Lyα fesc (dust, gas geom., kinem., )
• (QSOs and GRBs spectra
probing intervening IGM)
FGS/NIRISSNIRcam NIRSpec MIRI

•JWST will provide us with unique data
•Need models able to cope with these data (see R. Ellis talk)
•(But our general approach can be applied to any
spectro-photometric data-set)
•This requires overcoming typical limitations of exist. SED
modelling approaches:
‣ use of idealised physical models
‣ no consistent treatment of different galaxy
components (i.e. star, dust, gas, AGN)
‣ use of simplified statistical techniques, unable to deal
with complex multi-parameters models
Modelling (high-z) galaxies SEDs

A new SED analysis framework

Data science
StatisticsAstrophysics

Data science
StatisticsAstrophysics
BANGS
Bayesian ANalysis of Galaxies SEDs

Physical ingredients
Astrophysics
Stellar emission
HII regions
Dust attenuationAGN
Shocks
Dust emission
Semi-analytic
models
Hydro-dynamic
simulations
Phenomen.
models

Astrophysics
Stellar emission
HII regions
Dust attenuationAGN
Shocks
Dust emission
Semi-analytic
models
Hydro-dynamic
simulations
predictions from
sophisticated models
coherent treatment of
galaxy components
Phenomen.
models

Astrophysics
Stellar emission
HII regions
Dust attenuationAGN
Shocks
Dust emission
Semi-analytic
models
Hydro-dynamic
simulations
predictions from
sophisticated models
coherent treatment of
galaxy components
Phenomen.
models
See Anna Feltre’s
poster !

Statistical approach
Major driver of our stat. approach:

Statistical approach
x = 2
x = 2 ± 0.12
Math
Physics
Major driver of our stat. approach:
•Rigorous quantification of uncertainty challenging in
current era of large data-sets and multi-par. models
•Bayesian statistics better suited than point-wise
estimators (e.g. min. chi-square)

Data science with BANGS
Dimensionality
reduction
Interactive
visualization
Machine-learning
(e.g. classification,
clustering, GPR)
Combined model- and
data-driven approach to
galaxy SED analyses
Web services
(e.g. data-base)

BANGS workflow
Model predictions
Data fitting
(Bayesian statistics)
Galaxy physical
parametersGas-phase metallicity
α-elements abundance
Star formation and chemical
enrichment history
Stellar mass
Dust content
LOSVD
Star formation rate
Spectral
evolution model
(spectro-photometric)
Mock catalogues
Pseudo-observations
Observations
3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4
0
2
4
6
log λ [Å]
mag[arbitraryunits]
Input data
(spectra and photometry)
Noise
addition
OutputOutput

BANGS workflow
Model predictions
Data fitting
(Bayesian statistics)
Galaxy physical
parametersGas-phase metallicity
α-elements abundance
Star formation and chemical
enrichment history
Stellar mass
Dust content
LOSVD
Star formation rate
Spectral
evolution model
(spectro-photometric)
Mock catalogues
Pseudo-observations
Observations
3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4
0
2
4
6
log λ [Å]
mag[arbitraryunits]
Input data
(spectra and photometry)
Noise
addition
OutputOutput
Crucial testing on low-z “analogues” of high-z
galaxies !
(See Aida Wofford poster)

•UVUDF data (Rafelski+2015)
•(up to) 11 HST bands, from 0.2 to 1.5 micron
•9927 galaxies at 0 < z < 8
•Model adopted:
‣ stellar emission from CB14
‣ exp. delayed SFH + burst
‣ two-components dust attenuation (Chevallard+2013)
‣ IGM attenuation from Inoue+2014 (similar to Madau+1995)
‣ 6 adjustable parameters (including redshift)
Broad-band SED fitting
Validation Output Interpretation

0.2 0.1 0.0 0.1 0.2
(zbangs zrafelsky)/(1 + zrafelsky)
0
400
800
1200
1600
Numberofgalaxies
EAZY
BPZ
Photo-z estimates statistically consistent

Interpretation
0 2 4 6 8
zBANGS
0
2
4
6
8
zEAZY
1
10
100
EAZY
BANGS
OutputValidation

4000 8000 12000 16000
e↵/˚A (observed-frame)
0
25
50
75
f⌫/nanoJy
log(Z) = 13.44
h 2
i = 5.78
h 2
/(Ndata 1)i = 0.96 (Ndata = 7)
1655
6.4 7.2 8.0 8.8
0.8 1.6 2.4
2.4 1.6 0.8 0.0
8 9 10
1.8 1.2 0.6 0.0
11.2 10.4 9.6 8.8
log( S/yr)
0.8
1.6
2.4
z
2.4
1.6
0.8
0.0
logˆ⌧V
8
9
10
log(⌧/yr)
1.8
1.2
0.6
0.0
log(Z/Z)
6.4 7.2 8.0 8.8
log(M/M )
11.2
10.4
9.6
8.8
log(S/yr)
0.8 1.6 2.4
z
2.4 1.6 0.8 0.0
log ˆ⌧V
8 9 10
log(⌧/yr)
1.8 1.2 0.6 0.0
log(Z/Z )
⌧ . 1 Gyr
log M/M ⇠ 6.8 or ⇠ 8.4
z ⇠ 0.45 or ⇠ 2.4
⌧v . 0.16
1.2 . log(Z/Z ) . 0
s ⇠ 8.7
z ˆ⌧v SFH ⌧ Z/Z sSFRM/M
zˆ⌧vSFH⌧Z/ZSFRM/M

BANGS and JWST/NIRSpec
Simulations of observationsScience preparation
•NIRSpec will provide us with unprecedented data
•Need to search for best strategy to study high-z galaxies
w. NIRSpec
‣ Which features in galaxies SED can better constrain
different physical parameters, at different redshifts?
‣ Which mode, or combination of modes, is best? E.g.
low resolution vs high resolution, S/N thresholds?
‣ Which instrumental effects can hamper our
measurements?

Catalogue of
galaxy properties
and SEDs

Catalogue of
galaxy properties
and SEDs
Catalogue of
spectra
Simulation of
observations
ETC
3.0 3.5 4.0 4.5 5.0
µm
0
200
400
600
800
1000
1200
f⌫nanoJy

Catalogue of
galaxy properties
and SEDs
Catalogue of
spectra
Simulation of
astrophysical
scenes
Simulation of
observations
Simulation of
row and
processed data
ETC
IPS
B. Dorner
PhD thesis

ETC-like
simulations
“Full” IPS
simulations
Spectral fitting with BANGS to
retrieve input galaxy parameters
• PRISM
• SF galaxy @
z=9
• 109 Msun
• 100 ks

ETC-like
simulations
“Full” IPS
simulations
Spectral fitting with BANGS to
retrieve input galaxy parameters
• PRISM
• SF galaxy @
z=9
• 109 Msun
• 100 ks
Such detailed studies are crucial to prepare
JWST/NIRSpec GTO/GO observations and
maximize the scientific return from first
cycles of observations

Conclusions and future developments (1)
• JWST will provide us with unprecedented high quality data
on high-z galaxies
• Need sophisticated models + advanced statistical tools to
maximize information extracted from galaxy SEDs
• New framework for (high-z) SED analyses
• flexible physical model with coherent treatment of
different galaxy components
• can build mock catalogues of galaxies and fit (pseudo)
observations
• Approach validated on 10K galaxies at 0 < z < 8
• Combination of BANGS + other tools to simulate JWST/
NIRSpec observations and optimize future planning of GTO/
GO campaigns

Conclusions and future developments (2)
• Coherent implementation of physical recipes tailored at
reproducing SEDs of high-z galaxies:
• AGN emission (A. Feltre)
• emission from HII regions with variable chemical
abundances (J. Gutkin)
• emission from shock-ionized gas (A. Wofford)
• new prescription for high resolution UV (A. Vidal)
• “Data science”: development of combined data- and model-
driven approaches to SED analyses (D. Stenning)

Thanks

ESTEC_Oct_2015

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