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
2. Literature Values for κd Chris Clark Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
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)
4. Literature Values for κd Chris Clark Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
5. Mapping κd Within Galaxies Chris Clark Casasola+ (2017); Clark+ (2018); Casasola+ (in prep.) Clark+ (in prep.) M74 M83
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
8. Mapping κd Within Galaxies Chris Clark Casasola+ (2017); Clark+ (2018); Casasola+ (in prep.) Clark+ (in prep.) M74 M83
9. Metallicity Data in M74 & M83 Chris Clark De Vis+ (2019); Clark+ (in prep.) M74 M83
10. Metallicity Gradients Chris Clark De Vis+ (2019); Clark+ (in prep.)
11. Gaussian Process Regression Chris Clark Foreman-Mackey (dfm.io/george)
12. GPR - Metallicity Residuals Chris Clark De Vis+ (2019); Clark+ (in prep.) Radius (Deprojected R25) RA Dec
13. GPR - Metallicity Map for M74 Chris Clark De Vis+ (2019); Clark+ (in prep.)
14. GPR - Metallicity Map for M83 Chris Clark De Vis+ (2019); Clark+ (in prep.)
15. GPR – Works Reliably! Chris Clark De Vis+ (2019); Clark+ (in prep.)
16. All the Necessary Data Chris Clark Casasola+ (2017); Clark+ (2018); De Vis+ (2010); Casasola+ (in prep.) Clark+ (in prep.) M74 M83
17. Maps of κd within M73 & M83 Chris Clark Clark+ (subm.) M74 M83
18. κd vs ISM Surface Density Chris Clark Clark+ (subm.)
19. Alternate Model: DTM ∝ Density Chris Clark Clark+ (subm.) M83 M74
20. Results Summary Chris Clark Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
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
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…
23. The Only Solid Data is COBE! Chris Clark Meixner+ (2014); Roman-Duval+ (2017); Clark+ (in prep.) Herschel-SPIRE COBE-DIRBE
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
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
26. Results Summary Chris Clark Alton+ (2004); Demyk+ (2013); Köhler+ (2015); Clark+ (2016); Jones+ (2017); Clark+ (in prep.)
28. Alternate Models Chris Clark Clark+ (subm.) M74 DTM ∝ radius DTM ∝ ISM density “Toy” model M83 CHAOS Z
29. Alternate Models Chris Clark Clark+ (subm.) DTM ∝ radius DTM ∝ ISM density “Toy” model
30. CO r2:1 Regression Chris Clark Leroy+ (2012); Clark+ (subm.)
31. SED-Fitting Example Chris Clark Clark+ (in prep.)
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☉ )
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.
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)