Who is Karl? (2021 Edition)

Intro to the ISM*@ST group - 2021

Transcript

1. Who is Karl? Karl D. Gordon STScI, Baltimore, MD USA ISM*@ST Research Group 19 Apr 2021 kgordon@stsci.edu @karllark2000 karllark@github “Have Dust – Will Study”
3. Career Trajectory • BA in Physics (Wittenberg Univ. - Ohio) – Testing power electronics in high radiation environments – Hot star Teff, logg from spectroscopy • MS, PhD in Physics (Univ. of Toledo – Ohio) – Extended Red Emission in the Diffuse ISM – Dust radiative transfer – Echelle spectroscopy of stars with 1m telescope • Postdoc, Geoff Clayton (Louisiana State Univ) – Extinction curves, grant writing, observing • Research staff, George Rieke (Univ. of Arizona) – Instrumentation, data reduction pipelines • Astronomer (STScI)
4. Radiative Transfer • DIRTY RT Code – Gordon+; Misselt+ (2001) • Study albedo & g • Compute galaxy attenuation curves • Predict galaxy SEDs Reflection nebulae albedos Steinacker, Baes, & Gordon (2013, ARA&A, 51, 63) Gordon (2013, Astrophysics of Dust, 77)
5. Galaxies Attenuation Curves DirtyGrid: Attenuation + IR Emission Law, Gordon, & Misselt (2018, ApJS, 236, 32) Law, Gordon, & Misselt (2021, ApJ, submitted) Witt & Gordon (2000, ApJ, 528, 799)
6. Extinction Curves • Ultraviolet/optical – MW: optical (new broad features!) – SMC: 5→20 (bumps rare) – M31/M33 (like MW) • Near- & Mid-IR – NIR: in prep (Decleir et al.) – MIR: submitted (Gordon et al.) Gordon et al. (2009, ApJ, 705, 1320) Gordon et al. (2003, ApJ, 594, 279) Extension to 912 A – still rising! LMC/SMC do not follow MW R(V) relation Gordon et al. (2021, ApJ, submitted) First true diffuse ISM average – lower than recent lit
7. Expanded SMC Extinction Curve Sample Gordon et al. (202x, in prep.)
8. Sptizer/MIPS • Early hire onto the MIPS team @ Univ. of Arizona • 8 years – 4 before launch, 4 after • Instrument team data reduction pipeline lead – MIPS DAT (Gordon et al. 2005) – Team included Chad Engelbracht, Karl Misselt, Jane Morrison, James Muzerolle • Lead the 70 micron absolute flux calibration (Gordon et al. 2007) • Lots of science – Focused on nearby galaxies
9. Dust Emission – Optical & NIR • ERE (0.7 micron) – Extended Red Emission – Photoluminescence – Present in diffuse ISM – Carbonaceous grains? – Silicon nanoparticles? • New feature (1.5 micron) – Iron? • JWST GTO/ERS – Excitation wavelengths For aromatic/PAHs, aliphatics, new features! Gordon et al. (2000, ApJ, 544, 859) NW filament in reflection nebula NGC 7023
10. ERE Excitation Wavelength - Similar to H2 (FUV) Witt, Gordon, et al. (2006, ApJ, 636, 303)
11. Nearby Galaxies SINGS, LVL, KINGFISH SAGE-LMC, SAGE-SMC, SAGE-Spec, HERITAGE PHAT, PHATTER, LUVIT, HTTP, SMIDGE, METAL, Scylla
12. Dust Emission – Aromatic Features Aromatic (PAH) features show evidence of processing in Hard radiation fields Gordon et al. (2008, ApJ, 682, 336)
13. JWST/MIRI • Early hire to STScI MIRI team • 13+ years • Early years included all instrument aspects – Operations, user interaction, data reduction • Now focus on data reduction and calibration – Lead of JWST Baseline Pipeline → algorithms for all instruments – Lead of JWST AbsFlux WG → flux & surface brightness/flux calibration for all instruments
14. Dust Emission Far-Infrared Gordon et al. (2014, ApJ, 797, 85) Broken Emissivity model better than lots of cold dust See also: Roman-Duval et al. (2014, 2017) Chastenet et al. (2017, 2019, 2021) Clark et al. (2021)
15. The BEAST & MegaBEAST Gordon et al. (2016, ApJ, 826, 104) Baysian Extinction And Stellar Tool Focused on fitting millions of SEDs of stars in nearby galaxies PHAT, SMIDGE, Scylla, ... https://github.com/BEAST-Fitting/beast https://github.com/BEAST-Fitting/megabeast Broad wavelength coverage breaks degeneracies
16. DGFit: Dust Grain Modeling Gordon & Misselt (202x, in prep) Investigate dust grain properties in the Milky Way and nearby galaxies
17. Brewer
18. Woodworker
19. Runner
20. 2001 @ Univ. of Az
21. Thanks
22. Bayesian & Distributed Coding • Bayesian techniques for fitting – Provided the tool I’ve always wanted to handle (correlated) data uncertainties – Priors allow for quantifying assumptions – BEAST/MegaBEAST, DustBFF, DGFit, … • Distributed coding – DIRTY early example (two coders) – BEAST the forcing function – Python in Astronomy meeting – Skill, not just tools
23. Large Projects • Like to work with others • Instrument and Science Teams – Spitzer/MIPS, JWST/MIRI • Spitzer Legacies and Herschel Key Projects – Nearby Galaxies: SINGS, LVL, KINGFISH – Magellanic Clouds: SAGE-LMC, SAGE-SMC, SAGE-Spec • Hubble Large Programs – PHAT, HTTP, SMIDGE, METAL, Scylla, LUVIT, ... • ISM*@ST Group!