979 lines
81 KiB
Text
979 lines
81 KiB
Text
<!DOCTYPE html>
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<html lang="en" dir="ltr" prefix="content: http://purl.org/rss/1.0/modules/content/ dc: http://purl.org/dc/terms/ foaf: http://xmlns.com/foaf/0.1/ og: http://ogp.me/ns# rdfs: http://www.w3.org/2000/01/rdf-schema# schema: http://schema.org/ sioc: http://rdfs.org/sioc/ns# sioct: http://rdfs.org/sioc/types# skos: http://www.w3.org/2004/02/skos/core# xsd: http://www.w3.org/2001/XMLSchema# ">
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<head>
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<h1 class="nist-page__title">DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration</h1>
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<div class="nist-field__label">Published</div>
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<div class="nist-field__item"><time datetime="2024-12-11T12:00:00Z">December 11, 2024</time>
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<h3 class="nist-field__label">Author(s)</h3>
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<div class="nist-field__item">Benjamin M. Boyd, Gautham Narayan, Kaisey Mandel, Matthew Grayling, Aidan Berres, Mai Li, Aaron Do, ABHIJIT SAHA, Tim Axelrod, Thomas Matheson, Edward W Olszewski, ralph bohlin, Annalisa Calamida, Jenna Claver, <span class="nist-author">Susana Deustua</span>, Jay Holberg, Ivan Hubeny, John MacKenty, Konstantin Malanchev, Sean Points, Armin Rest, Elena Sabbi, Christopher Stubbs</div>
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<div class="text-with-summary">
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<h3>Abstract</h3>
|
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standards (16.5 < V < 19.5) alongside the three CALSPEC standards, from 900 Å to 32 μm. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters (log g, Teff, AV , RV ) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic HST/WFC3 UVIS and IR fluxes, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding < 4 mmag RMS on average from the UV to the NIR, achieved by jointly inferring changing zeropoints across HST cycles, as well as the F160W count rate non-linearity. Our GPU-accelerated implementation allows efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred SEDs from this model will be essential for calibrating next-generation surveys, including the James Webb Space Telescope, Vera Rubin Observatory's Legacy Survey of Space and Time, and the Nancy Grace Roman Space Telescope.
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<h3 class="nist-field__label">Keywords</h3>
|
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<div class="nist-field__item">white dwarfs, surveys, standard stars</div>
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|
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Boyd, B.
|
||
, Narayan, G.
|
||
, Mandel, K.
|
||
, Grayling, M.
|
||
, Berres, A.
|
||
, Li, M.
|
||
, Do, A.
|
||
, Saha, A.
|
||
, Axelrod, T.
|
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, Matheson, T.
|
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, Olszewski, E.
|
||
, Bohlin, R.
|
||
, Calamida, A.
|
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, Claver, J.
|
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, Deustua, S.
|
||
, Holberg, J.
|
||
, Hubeny, I.
|
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, MacKenty, J.
|
||
, Malanchev, K.
|
||
, Points, S.
|
||
, Rest, A.
|
||
, Sabbi, E.
|
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and Stubbs, C.
|
||
|
||
(2024),
|
||
DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration, Astrophysical Journal
|
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(Accessed March 4, 2025)
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If you have any questions about this publication or are having problems accessing it, please contact <a href="mailto:reflib@nist.gov" class="usa-link">reflib@nist.gov</a>.
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<div class="text-italic font-sans-2xs">
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Created December 11, 2024, Updated December 19, 2024
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