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Who is Karl? (2021 Edition)

Karl Gordon
November 03, 2021

Who is Karl? (2021 Edition)

Intro to the ISM*@ST group - 2021

Karl Gordon

November 03, 2021

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  1. Who is Karl? Karl D. Gordon
    STScI, Baltimore, MD USA
    ISM*@ST Research Group
    19 Apr 2021
    [email protected]
    “Have Dust – Will Study”

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  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)

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  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)

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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

    – 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

    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

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  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?

    – Excitation wavelengths For
    aromatic/PAHs, aliphatics, new
    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

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

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    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

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  14. Dust Emission
    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, ...
    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

    Bayesian techniques for fitting
    – Provided the tool I’ve always wanted to handle (correlated) data
    – 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

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  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!

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