Spatially-resolved Star Formation Rate andStellar Mass of Spiral Galaxies in the Local
Universe : Quantifying the Inside-outScenario of Disk Galaxies Formation
Abdurrouf (D1), Masayuki Akiyama
Astronomical Institute, Tohoku University

Galaxy Evolution Workshop : June 2, 2016

Outline• Introduction• Methodology• Result & Discussion• Conclusion

Galaxy Evolution Workshop : June 2, 2016

Model of Disk Assembly : Inside-out Scenario
Gas accretion• Galaxies assembled their disk by accreting gas
from cosmic web through gas accretion to theirhost DMH• The disk accretion rate is set by the rate at
which the DMH accretes mass, and the rate ofgas cooling• The gas cools onto the disk and form stars witha radial distribution set by angular momentum
distribution of the halo
• Gas accreted at later time and has high angularmomentum will resides in the outer region of
the disk

Dissipatingand cooling of gas• This lead to the “inside-out” scenario of

disk assembly (e.g. Kauffmann, 1996; Cole et al.,

2000; van den Bosch, 2002; Dutton & van den Bosch, 2009)(Mo, H., van den Bosch, F., White, S., 2010)

Testing the Inside-out Scenario of Disk Galaxy
Formation• The inside-out growth of disk galaxy should imprint some radially distributed
properties (e.g. age, metallicity, stellar mass, and star formation activity)• Inside-out disk galaxy assembly  SF activity is centrally concentrated in the pastand gradually less concentrated/extended at later time
• Two parameters can be used as test for the scenario :o 𝑴∗ distribution  past record of star formation activity distributiono SFR distribution  current distribution of star formation activity• Inside-out scenario  SFR distribution is going to be more extended than 𝑴∗
distribution at later time

Observational Result at 𝒛~𝟏 : 𝑯𝜶 Sizes
vs Stellar Continuum Sizes

57 starforminggalaxies

SFR[𝑀⊙ 𝑦𝑟 −1 ](Nelson, E. J., et al. 2012,ApJ, 747, 28)

𝑯𝜶 eff-rad [kpc]• 𝐻𝛼 sizes generally
track the F140W(rest-frame R band)sizes but withtypically larger (~1.3time)

0.8 < z < 1.3

Rest-frame R eff-rad [kpc]𝑀∗ [𝑀⊙ ]No dust-correction is applied in this result

Motivation : How SFR and 𝑴∗
Distribution of Local Spiral Galaxies ?

• Future work : with currently
established method and by mean oflarge number of photometric data,then calculating SFR and 𝑀∗
distribution of large number ofgalaxies over large redshift range, todeeply study their evolution

Statistic is the key

How it looks
like at z~𝟎 ?

𝑯𝜶 eff-rad [kpc]• Establishing method to resolve SFRand 𝑀∗ in the galaxies pixel-to-pixel
SED Fitting• Massive local spiral galaxies mighthighly experienced inside-out growthin the past𝒛~𝟏57 starforminggalaxies

Rest-frame R eff-rad [kpc]Outline• Introduction• Methodology• Result & Discussion• Conclusion

Galaxy Evolution Workshop : June 2, 2016

Data Sample
GALEXGalaxies at 0.01<z<0.02with log(𝑴∗ [𝑴⊙ ])>10.5• We use data from GALEX and SDSS
(7bands, 1344 ≤ 𝜆 ≤10000 Å)• Data sample : 118 relatively face-on
spiral galaxies, located at0.01<z<0.02, more massive thanlog(𝑀∗ [𝑀⊙ ])>10.5, with total
MPA/JHU Catalog for 0.01<z<0.02
S/N>10 in 7 bands

Edge-onFace-onSDSS Galaxy Zoo’s based
Morphological Classification

Methodology
DataAnalysisPixel-topixel SED
Fitting
SED ModelsConstruction

Methodology(1) : Data Analysis
New pixels binning methodwith 3 criteria : “closeness”;
similarity of SED’s shape; s/N

Pixels binning
7 bands resolved photometric fluxes
Masking forgroundstars
7 segmentation map by SEXTRACTOR  summingGet 7 bands fluxes andtheir uncertainties

Defining galaxy’s
regionPSF Matching using convolution kernels
created using PSFMATCH Iraf

PSF Matching

Methodology(2) : SEDs Models Construction
𝑓𝑚𝑜𝑑,𝑖 =Random 4 parameters(Z,𝝉,E(B-V),age)‫𝜆𝑑 𝜆 𝑖𝑇𝜆 𝜆 𝜆𝑓 ׬‬‫𝜆𝑑 𝜆 𝑖𝑇𝜆 ׬‬

Interpolate models forrandom parameters

Number of generated
random models : 200000

Redshifting
7 bands filtering
𝑯𝟎 = 𝟕𝟎𝐤𝐦𝒔−𝟏 𝐌𝐩𝐜 −𝟏𝜴𝒎 = 𝟎. 𝟑
𝜴𝜦 = 𝟎. 𝟕

𝐹𝜆 𝜆𝑒 (1 + 𝑧) =Calzetti 2000

Integrating throughfilter transmission
curvesModel interpolation

Number of parent models : 193600

𝐿𝜆 (𝜆𝑒 )4𝜋𝑑𝐿 2 (1 + 𝑧)

Cosmologicalredshifting

GALAXEV
Reddening bydust
Rest-frame
spectral models

using Calzetti 2000

using GALAXEV (Bruzual & Charlot 2003)

Methodology(3) : Pixel-to-Pixel SED Fitting
Posterior mean :SFR 𝑃𝐷𝐹

𝑀∗ 𝑃𝐷𝐹

Generate 100 random SEDs
following Normal distribution
around observed SED

Calculate SFR and 𝑀∗
uncertainties using
Monte-Carlo methodwith 𝜃 ∶ 𝑆𝐹𝑅 𝑎𝑛𝑑 𝑀∗
𝑴∗ = 𝑴𝒐𝒅𝒆𝒍′ 𝒔 𝒔𝒕𝒆𝒍𝒍𝒂𝒓 𝒎𝒂𝒔𝒔 × 𝒔
𝑺𝑭𝑹 =𝑴∗
𝟏 − 𝐞𝐱𝐩 −𝒂𝒈𝒆/𝝉

×𝐞𝐱𝐩(−𝒂𝒈𝒆/𝝉)𝝉𝑁 = 200000 models

with normalization (𝒔),random-uniform around :𝜒𝑖 2Calculate posteriormean of SFR and 𝑀∗

𝑠𝑙𝑒𝑎𝑠𝑡Calculate weight ofeach model, 𝑤𝑖

Model SED

Observed SED

Calculate 𝜒𝑖 2 of eachmodelwith Bayesian Statistic

Case Example : M51 Galaxy
FUV Flux

𝟐𝟒𝝁𝒎 Flux𝑴∗

SFR

Case Example M51 : Why Calzetti (2000) Dust Extinction Law ?
Milky Way dust-extinction law (Seaton(1979) + Cardelli(1989))

Calzetti (2000)

Outline• Introduction• Methodology• Result & Discussion• Conclusion

Galaxy Evolution Workshop : June 2, 2016𝑴∗ and SFR Density Radial Profiles
SFR

𝑀∗ density and SFR density radial profile ofgalaxies sample show declining toward theoutskirt

𝑴∗

Elliptical annuli

Radial profile

SFR Radial Profile vs 𝑴∗ Radial Profile• The average SFR density radialprofile follows the average𝑀∗ density radial profile, but withrather extended profile• This results may confirm theinside-out scenario of disk
galaxy formation, which suggeststhat present star formation in thegalaxies occurs in rather extended(more outskirt) than past starformation

sSFR Radial Profiles• sSFR radial profile ofspiral galaxies in thesample show relatively
flat radial profile outsideof about 5kpc, but
suppress in the central
region• This profile shows that
SFR being suppressedfrom inside-to-outside 
inside-out quenchingThis research (Spiral at 0.01<z<0.02)

log 𝑠𝑆𝐹𝑅 [𝐺𝑦𝑟 −1 ]IFU Survey (0.005<z<0.03)

1𝑒 −12 𝑦𝑟 −1

Radial distance (HLR)
R. M. González Delgado, et al. 2016,A&A 590, A44

SFR eff-radius vs 𝑴∗ eff-radius• Majority of galaxies atz~1 have SFR eff-rad >𝑀∗ eff-rad, but some of
the galaxies show SFR
eff-rad < 𝑀∗ eff-rad(Nelson, E. J., et al. 2012,ApJ, 747, 28)This research (at 0.01<z<0.02)

𝑯𝜶 eff-rad• Almost all of the galaxies
in the sample (spiralgalaxies, 0.01<z<0.02)
have SFR effective radiuslarger than 𝑀∗ effectiveradiusat z~𝟏57 starforminggalaxies

Rest-frame R eff-rad(Nelson, E. J., et al. 2012, ApJ, 747, 28)

SFR_inside vs SFR_outside of Half-Mass Radius• Majority of galaxies
in the sample(0.01<z<0.02) have
SFR-outside larger
than SFR-inside of𝑀∗ effective radius(half-mass radius)Galaxy with High and Low (SFR_inside/SFR_outside)
optic

FUV

Low (SFR_inside/SFR_outside)

High (SFR_inside/SFR_outside)

1

6109

114

27110110

115

38111

116

49112

117

510113118

Sorted from highest tolowest of (SFR_in/SFR_out)
Some of the galaxies in
10 lowest ratio areBar-galaxies

Bar- and Non Bar-Galaxies(1)Gal067 : Non Bar-galaxy

Gal179 : Non Bar-galaxy

Gal179 : Non Bar-galaxy

SFR

𝑴∗

SFR𝑴∗

sSFR

Bar- and Non Bar-Galaxies(2)Gal013 : Bar-galaxy

Gal024 : Bar-galaxy

Gal033 : Bar-galaxy

SFR𝑴∗

SFR𝑴∗

sSFR

Higher and lower mass bin : Resolved “Downsizing” ?
Both stellar mass bins have
similar sSFR profile

SFR density of higher mass bingalaxies typically higher at all radii𝑀∗ density of higher mass binGalaxies typically higher at all radii

SFR59

𝑴∗

59

sSFR profile of higher mass bin
typically lower in all radius“Downsizing” in spatial scalesSFRIntegrated SFMS vs Resolved SFMS

Integrated SFMS

118 galaxies• Integrated SFMS and resolved SFMS have very
similar characteristic (slope, zero-point, andscatter)  SFMS is preserved up to the spatialscale• Cano-Díaz, M. et al. (2016) also found thesimilarity between integrated and resolved
SFMS of local galaxies (integrated :𝛼 =0.81 𝛽 = −8.34 𝜎 = 0.20; resolved : 𝛼 =0.72 𝛽 = −7.95 𝜎 = 0.16 )  global SFMS
may be derived from local SFMS

Resolved SFMS - bin

4782 binslog 𝑆𝐹𝑅 𝑀⊙ 𝑦𝑟 −1 = α log 𝑀∗ 𝑀⊙ + 𝛽
Coefficient

Integrated SFMS

Resolved SFMS

Slope (𝛼)

0.82

0.99

Zero-point (𝛽)-8.63-10.52Standard deviation (𝜎)

0.17

0.20

(Cano-Díaz, M.
et al. 2016)

Outline• Introduction• Methodology• Result & Discussion• Conclusion

Galaxy Evolution Workshop : June 2, 2016

Conclusion• From analyzing 118 spiral galaxies at 0.01<z<0.02, we found that :oSFR distribution generally follow 𝑀∗ distribution, but slightly more extended

oAverage sSFR radial profile is relatively flat in the outskirt (outside of about 5kpc) butsuppress in the centeroMajority of galaxies show that total SFR outside larger than total SFR inside of halfmass radiusoMore massive galaxies tend to be quenched faster in all radius compare to less massivegalaxies  resolved “Downsizing”

oIntegrated SFMS and resolved SFMS characteristics are similar  global SFMS may bederived from more fundamental relation in the spatial scale (local SFMS)• Overall, this results may confirm other observation results, for galaxies at local universe(Munoz-Mateos, J. C. et al. 2007; Wang, J. et al. 2011; Perez, E. et al. 2013) as well as forgalaxies at z~1 (Nelson, E. J. et al. 2012), which were said to be supporting the inside-out
scenario of disk galaxy formation

Thank you for your attention !!!

Galaxy Evolution Workshop : June 2, 2016

Pixels Binning Technique• This research used new pixels binning techniquewhich considers 3 criteria :• “closeness”• “similarity SED’s shape” among bin’s22members (𝜒12< 𝜒thresh)• Total S/N of bin > S/N_thresh• Throughout this research, binning with
S/N_thresh=10 and chi-square limit of 30 is used
• Chi-square equation to test similarity of SED’s
shape :

𝜒122 =෍
𝑖𝑓2,𝑖 − 𝑠12 𝑓1,𝑖𝜎1,𝑖 2 + 𝜎2,𝑖 2σ𝑖2with

𝑠12 =𝑓2,𝑖 𝑓1,𝑖𝜎1,𝑖 2 + 𝜎2,𝑖 2𝑓1,𝑖 2
σ𝑖𝜎1,𝑖 2 + 𝜎2,𝑖 2

Model Interpolation – in 4D (Z,tau,E(B-V),age)• Parent models (193600) are generated using
GALAXEV (Bruzual & Charlot 2003), withparameters :o Z : 0.004, 0.008, 0.02, and 0.05
o Tau : [0.05:2] with delta_tau=0.05
o E(B-V) : [0:0.6] with delta_color_excess=0.05
o Age : [0.25:13.75] with delta_age=0.25• For each metallicity, Model’s flux and stellar
mass are interpolated using method of tricubic
interpolation (in 3 dimension: Tau, E(B-V), andAge), then for some fix values of (tau, E(B-V),
and age), cubic spline interpolation is done tointerpolate for random metallicity

Methodology(4) : Distribution of SFR and 𝑴∗ among
Galaxy’s PixelsCalculate pixel’s SFR and𝑴∗ uncertainties

Distribute SFR and 𝑴∗ ofeach bin into its constituent
pixelsAssuming :𝑺𝑭𝑹𝒊 ∝ 𝑭𝑼𝑽 𝑭𝒍𝒖𝒙𝒊 and 𝑴∗𝒊 ∝ 𝒛 𝑭𝒍𝒖𝒙𝒊

Case Example M51(1) : Why Calzetti (2000) Dust Extinction Law ?
Milky Way dust-extinction law (Seaton(1979) + Cardelli(1989))

Calzetti (2000)

Bayesian SED Fitting : Model’s Weight – M51 Case
Maximum likelihood

𝝁 = 𝟎. 𝟎𝟏
𝝈 = 𝟎. 𝟓

Bayesian with 𝑷 𝜽 𝑿 ∝ 𝒆𝒙𝒑 (−𝝌𝟐 /𝟐)

𝝁 = 𝟎. 𝟎𝟎𝟏
𝝈 = 𝟎. 𝟒

Bayesian with 𝑷 𝜽 𝑿 ∝ 𝟏/𝝌𝟐

𝝁 = −𝟎. 𝟎𝟒
𝝈 = 𝟎. 𝟐

Bayesian SED Fitting : Model’s Weight – This Sample
Bayesian with 𝑷 𝜽 𝑿 ∝ 𝒆𝒙𝒑 (−𝝌𝟐 /𝟐)

Bayesian with 𝑷 𝜽 𝑿 ∝ 𝟏/𝝌𝟐

SFRs and stellar masses of118 spiral galaxies sample
The SFRs and stellar masses
are total within SDSS fiber
diameter (3 arcsec=2 pixel)
Better consistency found for𝑃 𝜃 𝑋 ∝ 1/𝜒 2