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”
   

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3. Career Trajectory
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   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
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   Postdoc, Geoff Clayton (Louisiana State Univ)
   – Extinction curves, grant writing, observing
   ●
   Research staff, George Rieke (Univ. of Arizona)
   – Instrumentation, data reduction pipelines
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   Astronomer (STScI)
   

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4. Radiative Transfer
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   DIRTY RT Code
   – Gordon+; Misselt+ (2001)
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   Study albedo & g
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   Compute galaxy attenuation curves
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   Predict galaxy SEDs
   Reflection nebulae albedos
   Steinacker, Baes, & Gordon (2013, ARA&A, 51, 63)
   Gordon (2013, Astrophysics of Dust, 77)
   

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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)
   

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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
   

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7. Expanded SMC Extinction Curve Sample
   Gordon et al. (202x, in prep.)
   

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8. Sptizer/MIPS
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   Early hire onto the MIPS team @ Univ. of Arizona
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   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)
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   Lots of science
   – Focused on nearby galaxies
   

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9. Dust Emission – Optical & NIR
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   ERE (0.7 micron)
   – Extended Red Emission
   – Photoluminescence
   – Present in diffuse ISM
   – Carbonaceous grains?
   – Silicon nanoparticles?
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   New feature (1.5 micron)
   – Iron?
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   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
   

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10. ERE Excitation Wavelength - Similar to H2 (FUV)
    Witt, Gordon, et al. (2006, ApJ, 636, 303)
    

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11. Nearby Galaxies
    SINGS, LVL, KINGFISH
    SAGE-LMC, SAGE-SMC, SAGE-Spec,
    HERITAGE
    PHAT, PHATTER, LUVIT,
    HTTP, SMIDGE, METAL, Scylla
    

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12. Dust Emission – Aromatic
    Features
    Aromatic (PAH) features show
    evidence of processing in
    Hard radiation fields
    Gordon et al. (2008, ApJ, 682, 336)
    

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13. JWST/MIRI
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    Early hire to STScI MIRI team
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    13+ years
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    Early years included all instrument aspects
    – Operations, user interaction, data reduction
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    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
    

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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)
    

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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
    

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16. DGFit: Dust Grain Modeling
    Gordon & Misselt (202x, in prep)
    Investigate dust grain properties in the Milky Way and nearby galaxies
    

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17. Brewer
    

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18. Woodworker
    

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19. Runner
    

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20. 2001 @ Univ. of Az
    

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21. Thanks
    

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22. Bayesian & Distributed Coding
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    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, …
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    Distributed coding
    – DIRTY early example (two coders)
    – BEAST the forcing function
    – Python in Astronomy meeting
    – Skill, not just tools
    

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23. Large Projects
    ●
    Like to work with others
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    Instrument and Science Teams
    – Spitzer/MIPS, JWST/MIRI
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    Spitzer Legacies and Herschel Key Projects
    – Nearby Galaxies: SINGS, LVL, KINGFISH
    – Magellanic Clouds: SAGE-LMC, SAGE-SMC, SAGE-Spec
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    Hubble Large Programs
    – PHAT, HTTP, SMIDGE, METAL, Scylla, LUVIT, ...
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    ISM*@ST Group!
    

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