Mapping κd in Nearby Galaxies

Talk describing work presented in C J R Clark et al (2019), presented as a seminar given at University College London in April 2019, and at the East Asian Observatory in October 2019. An abridged version of this talk was presented at the following conferences:
"Cosmic Dust - Origin, Applications, and Implications" conference, Copenhagen, 2018;
"Linking the Milky Way and Nearby Galaxies" conference, Helsinki, 2019.

Transcript

1. Mapping κd
   in
   Nearby Galaxies
   Chris Clark
   Pieter De Vis
   Simone Bianchi
   & the DustPedia team
   

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2. Literature Values for κd
   Chris Clark
   Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
   

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3. Estimating κd
   with the HRS
   Chris Clark
   James+ (2002); Ciesla+ (2012); Clark+ (2016)
   κ
   500
   = 0.051 m2 kg-1
   (± 0.24 dex)
   Estimating κd
   with the HRS
   Chris Clark
   James+ (2002); Ciesla+ (2012); Clark+ (2016)
   κ500
   = 0.051 m2 kg-1
   (± 0.24 dex)
   

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4. Literature Values for κd
   Chris Clark
   Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
   

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5. Mapping κd
   Within Galaxies
   Chris Clark
   Casasola+ (2017); Clark+ (2018); Casasola+ (in prep.) Clark+ (in prep.)
   M74
   M83
   

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7. The DustPedia Database
   • The DustPedia (Davies+, 2017)
   covers all 875 nearby (D<40 Mpc)
   extended (1’ < D25 < 1°) galaxies
   observed by Herschel.
   • Standardised imagery & photometry
   spanning 42 UV–microwave bands
   (Clark+, 2018).
   • Homogenised atomic & molecular gas
   values for 764 & 255 DustPedia
   galaxies respectively (Casasola+, in
   prep.; De Vis+, 2019).
   • 10000 consistently-determined gas-
   phase metallicity datapoints (from
   IFU, slit, and fibre spectra) for 492
   DustPedia galaxies (De Vis+ 2019).
   UV-NIR-FIR montage of some of the
   galaxies in the DustPedia database
   Chris Clark
   Clark+ (2018); De Vis+ (2019); Casasola+ (in prep.)
   .com
   

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8. Mapping κd
   Within Galaxies
   Chris Clark
   Casasola+ (2017); Clark+ (2018); Casasola+ (in prep.) Clark+ (in prep.)
   M74
   M83
   

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9. Metallicity Data in M74 & M83
   Chris Clark
   De Vis+ (2019); Clark+ (in prep.)
   M74
   M83
   

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10. Metallicity Gradients
    Chris Clark
    De Vis+ (2019); Clark+ (in prep.)
    

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11. Gaussian Process Regression
    Chris Clark
    Foreman-Mackey (dfm.io/george)
    

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12. GPR - Metallicity Residuals
    Chris Clark
    De Vis+ (2019); Clark+ (in prep.)
    Radius (Deprojected R25)
    RA
    Dec
    

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13. GPR - Metallicity Map for M74
    Chris Clark
    De Vis+ (2019); Clark+ (in prep.)
    

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14. GPR - Metallicity Map for M83
    Chris Clark
    De Vis+ (2019); Clark+ (in prep.)
    

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15. GPR – Works Reliably!
    Chris Clark
    De Vis+ (2019); Clark+ (in prep.)
    

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16. All the Necessary Data
    Chris Clark
    Casasola+ (2017); Clark+ (2018); De Vis+ (2010); Casasola+ (in prep.) Clark+ (in prep.)
    M74
    M83
    

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17. Maps of κd
    within M73 & M83
    Chris Clark
    Clark+ (subm.)
    M74 M83
    

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18. κd
    vs ISM Surface Density
    Chris Clark
    Clark+ (subm.)
    

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19. Alternate Model: DTM ∝ Density
    Chris Clark
    Clark+ (subm.)
    M83
    M74
    

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20. Results Summary
    Chris Clark
    Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
    

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21. Next: the SMC, at All Scales
    Chris Clark
    Meixner+ (2014); Roman-Duval+ (2017); Williams+ (2018); Clark+ (in prep.)
    Herschel only; no faint+large scales
    

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22. So, You Want To Study Dust in the MCs?
    Chris Clark
    Roman-Duval+ (2017); Clark+ (in prep.)
    • Herschel!
    • …Except faint structure at the edges got removed as ‘background’, as
    the map was too small. Plus large-scale features get filtered out.
    • Okay, Planck then!
    • …And Planck is great! Except it goes no shorter than 350 um, so you
    have no way of constraining dust temperature, and therefore mass.
    • How about Spitzer?
    • …Which has similar background-level problems to Herschel. Plus,
    severe non-linearity issues at high surface brightness for 160 um.
    • But there’s always IRAS, right?
    • …Unless you want to observe something that is extended and has very
    high surface brightness. Like the Magellanic Clouds.
    • Urm, I suppose I could try using Akari?
    • …
    • Good point. How about JCMT? Or ISO?
    • …Never observed more than tiny parts of the Clouds.
    • I suppose that leaves…
    

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23. The Only Solid Data is COBE!
    Chris Clark
    Meixner+ (2014); Roman-Duval+ (2017); Clark+ (in prep.)
    Herschel-SPIRE COBE-DIRBE
    

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24. Combine All The Data
    Chris Clark
    Clark+ (in prep.)
    COBE
    Far-infrared data,
    large angular scales
    IRAS
    Far-infrared data,
    medium angular scales
    Planck
    Submm data, large &
    medium angular scales
    COBE + IRAS
    FIR data, large and
    medium angular scales
    COBE + IRAS
    + Planck
    FIR-submm data, large &
    medium angular scales
    Herschel
    FIR-submm data,
    small angular scales
    COBE + IRAS
    + Planck + Herschel
    FIR-submm data, large &
    medium & small angular scales
    

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25. Next: the SMC, at All Scales
    Chris Clark
    Meixner+ (2014); Roman-Duval+ (2017); Williams+ (2018); Clark+ (in prep.)
    Herschel only; no faint & large scales Herschel et al; Fourier-combined
    

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26. Results Summary
    Chris Clark
    Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
    

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28. Alternate Models
    Chris Clark
    Clark+ (subm.)
    M74
    DTM ∝ radius DTM ∝ ISM density “Toy” model
    M83
    CHAOS Z
    

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29. Alternate Models
    Chris Clark
    Clark+ (subm.)
    DTM ∝ radius DTM ∝ ISM density “Toy” model
    

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30. CO r2:1
    Regression
    Chris Clark
    Leroy+ (2012); Clark+ (subm.)
    

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31. SED-Fitting Example
    Chris Clark
    Clark+ (in prep.)
    

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32. Dust-to-Metals via Depletions
    • Wiseman+ (2016) and De Cia+ (2016) find DTM varies
    with metallicity, from DLA depletions; but for metallicities
    of >0.1 Z☉
    this variation is less than factor of ≤2.
    • Jenkins+ (2009) find Milky Way variation of factor ≤2.7.
    Chris Clark
    De Cia+ (2016); Wiseman+ (2016); Clark+ (in prep.)
    Figure 7 from Wiseman+ (2016) Figure 15 from De Cia+ (2016)
    log10
    (Z/Z☉
    )
    

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33. Dust-to-Metals in Simulations
    6 7 8 9 10
    12+log10
    (O/H)
    Chris Clark
    McKinnon+ (2016); Popping+ (2017); Clark+ (in prep.)
    Figure 5 from Popping+ (2017) Figure 15 from McKinnon+ (2016)
    • Popping+ (2017) find DTM varies by factor of <4 at
    metallicities >0.1 Z☉
    in semi-analytic models.
    • McKinnon+ (2016) find DTM varies by factor of ≤3.5 at
    z<0.5 in hydrodynamical zoom-in simulations.
    

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34. Dust-to-Metals in THEMIS
    Chris Clark
    Jones+ (2017); Jones+ (2018); Clark+ (in prep.)
    • Dust-to-metals expected to vary by factor of ~3.6 in
    THEMIS dust model (Jones+ 2017;2018).
    Table 3 from
    Jones+ (2018)
    

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