the variation of the dust-to-metals ratio in resolved nearby ...i-da chiang (cass, ucsd) the...

The Variation of the Dust-to-Metals Ratio in ResolvedNearby Galaxies

I-Da Chiang1, Karin M. Sandstrom1, Jérémy Chastenet1,L. Clifton Johnson2, Adam K. Leroy3 and Dyas Utomo3

1University of California, San Diego; 2Northwestern University;3The Ohio State University

Dusting the Universe – March 4th, 2019

I-Da Chiang (CASS, UCSD) The Resolved Dust-to-Metals Relation March 4th, 2019 1 / 15

The Life Cycle of Dust

The life cycle of dust depends on various dust formation anddestruction mechanisms. (Dwek 1998; Hirashita 1999; Asano et al. 2013 and more)

The balance between these mechanisms reflects on/depends on theISM properties, especially the mass ratios between dust, gas, andmetals.

To better understand the life cycle of dust, we would like toinvestigate how the dust-to-gas ratio (DGR) and dust-to-metals ratio(DTM) depends on ISM properties.(Issa et al. 1990; Dwek 1998; Lisenfeld & Ferrara 1998; Hunt et al. 2005; Galliano et al. 2008; Zhukovska etal. 2008, 2016; Leroy et al. 2011; Draine et al. 2014; Fisher et al. 2014; Gordon et al. 2014; Rémy-Ruyer etal. 2014; Giannetti et al. 2017; Roman-Duval et al. 2017; Vílchez et al. 2018; Vis et al. 2019 and more)


DGR Variation Across ∼ 2 dex Metallicity Range

∆ log(DGR) = [2.02 ± 0.28] × ∆(12 + log(O/H)

)Rémy-Ruyer et al. (2014)

∆ log(DGR) = [2.45 ± 0.12] × ∆(12 + log(O/H)

)Vis et al. (2019)

In the integrated studies of hundreds of nearby galaxies, it is shown thatDGR is non-linearly related with 12+log(O/H)....which indicates a varying DTM!


Moving towards Spatially Resolved Study

Dust and metal depends on localphysical environments.

ISM density, phase, composition......

Overall DTM depends on method ofaveraging

Choice of representative metallicity

Possible overestimation in dusttemperature in integrated study

Utomo, Chiang et al. (2019)

fH2 in M101 center

Walter et al. (2008), Leroy et al. (2009)

12+log(O/H) in M101

Croxall et al. (2016)


M101 Data

12 + log(O/H) = [8.716 ± 0.023] − [0.027 ± 0.001] (R/kpc)(CHAOS, Croxall et al. 2016)

IRAC, MIPS and GALEX data(LVL, Dale et al. 2009)

SPIRE 500 beam FWHM ∼ 1.17 kpc


Modelling Dust Emission

The modified blackbody modelIν(λ) = κ(λ)ΣdBν(Td , λ)

κ(λ) [cm2 g−1]: The dust emissivity.

κ(λ) =

{κ0(λ0

λ )β λ < λc

κ0(λ0λc

)β(λc

λ )β2 λ ≥ λc

Σd [M� pc−2]: Dust surface density.

Bν(Td , λ) [MJy sr−1]: Blackbody SED at dusttemperature Td .

An multi-variate likelihood fitting approach witha complete covariance matrix is used.(Gordon et al. 2014; Chastenet et al. 2017; Chiang et al. 2018;Utomo, Chiang et al. 2019)

Chiang et al. (2018)


A Variable DTM in M101

(Chiang et al. 2018; Rémy-Ruyer et al. 2014; Vis et al. 2019)

Our best-fit result shows:

∆ log(DGR) =

{ [1.7± 0.1

]× ∆

(12 + log(O/H)

), Full range[

1.9± 0.1]× ∆

(12 + log(O/H)

), 12 + log(O/H) ≥ 8.2


Other Environmental Variables

The other environmental variables that can trace DTM-changingmechanisms...

H2 fraction (fH2): as a tracer of ISM density, it can track the efficiencyof accretion of gas-phase metals onto existing dust grains.

Stellar mass surface density (Σ∗): as a tracer of dust enrichment fromstellar sources.

Star formation rate surface density (ΣSFR): as a tracer of overall effectmade by SNe.

We calculate the Spearman’s rank correlation coefficient (ρS) andcorresponding p-values between DTM and tracers.


Other Environmental Variables: The Correlations

However, all tracers and DTM itself have strong correlations with radius.

We thus remove their radial trend with linear regression to investigate theirsecond-order correlation

0.2 0.1 0.0 0.1 0.2 0.3

log10fH2 res.

0.2

0.1

0.0

0.1

log 1

0DTM

res.

S = 0.26p-value 1

0.2 0.0 0.2 0.4 0.6

log10 res.

S = -0.05p-value = 0.12

0.5 0.0 0.5 1.0 1.5

log10 SFR res.

S = -0.08p-value 1


fH2 shows the strongest correlation with DTM in the residual, and the p-valueconfirms its significance.

Since fH2 is our tracer for ISM density, we consider accretion of gas-phase metalsto be a possible cause to the variable DTM in M101.

Vílchez et al. (2018) reached a similar conclusion in their recent study ofM101 & NGC628.


Other Environmental Variables: fH2

We compare our results to the resolvedstudy in M31 by Draine et al. (2014).

In the region with similar metallicity, theone with higher fH2 (M101) has higherDGR.

Interestingly, DGR is more consistent inthe region where M101 and M31 havesimilar fH2 .

7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.6

10 4

10 3

10 2

DGR

M101 (C18)M31 (D14)

7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.612 + log(O/H)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

f H2

D14 Data

M101 fH2 (C18)M31 12+log(O/H) range (D14)M31 fH2 range (D14)



Next target: IC342

12 + log(O/H) = 9.13 − 0.11 (R/kpc)(MaNGA, Kreckel et al., in prep.)

WISE and GALEX data(z0MGS, Leroy, Sandstrom et al., submitted)

SPIRE 500 beam FWHM ∼ 380 pc (M101: ∼ 1.17 kpc)


The z = 0 Multi-wavelength Galaxy Synthesis (z0MGS)

Want to learn about the WISE and GALEX part? Visit the z0MGS poster!(Leroy, Sandstrom et al. submitted; Chiang et al. in prep.; Chastnet et al. in prep.)


Summary

Our observed DTM is not constant within M101. The best-fit resultshows:∆ log(DGR) ={ [

1.7± 0.1]× ∆

(12 + log(O/H)

), Full range[

1.9± 0.1]× ∆

(12 + log(O/H)

), 12 + log(O/H) ≥ 8.2

According to the correlation between DTM and physical tracers, weargue that the accretion of metals in gas-phase onto existing dustgrains could be a cause of this variable DTM.

More spatially resolved ISM science coming up with our newmulti-wavelength images!


Thanks for your attention!!

This talk is mainly based on the following papers:I-D. Chiang, K. M. Sandstrom, J. Chastenet, L. C. Johnson, A. K.Leroy, D. Utomo, “The Spatially Resolved Dust-to-metals Ratio inM101”, 2018, ApJ, 865, 117

D. Utomo, I-D. Chiang, A. K. Leroy, K. M. Sandstrom, J. Chastenet,“The Resolved Distributions of Dust Mass and Temperature inLocal Group Galaxies”, 2019, arXiv:1902.08629 (accepted by ApJ)

We thank the support from the National Science Foundation andNASA.


Links to Our Papers

I-D. Chiang, K. M. Sandstrom, J.Chastenet, L. C. Johnson, A. K. Leroy,D. Utomo, “The Spatially ResolvedDust-to-metals Ratio in M101”, 2018,ApJ, 865, 117

D. Utomo, I-D. Chiang, A. K. Leroy, K.M. Sandstrom, J. Chastenet, “TheResolved Distributions of Dust Massand Temperature in Local GroupGalaxies”, 2019, arXiv:1902.08629(accepted by ApJ)


the variation of the dust-to-metals ratio in resolved nearby ...i-da chiang (cass, ucsd) the...

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