DAmodel: hierarchical Bayesian modelling of DA white dwarfs for spectrophotometric calibration

ABSTRACT We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside three CALSPEC standards, from 912 Å to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zero points and WD parameters (surface gravity $\log g$, effective temperature $T_{\text{eff}}$, extinction $A_V$, dust relation parameter $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic Hubble Space Telescope Wide Field Camera 3 (HST/WFC3) UVIS and IR photometry, HST/STIS UV spectroscopy, and ground-based optical spectroscopy to sub-per cent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables 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 spectral energy distributions from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory’s Legacy Survey of Space and Time and the Nancy Grace Roman Space Telescope.

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Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-04-16
DOI
https://doi.org/10.1093/mnras/staf629
Citations
3
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
2.26

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article

DAmodel: hierarchical Bayesian modelling of DA white dwarfs for spectrophotometric calibration

Gautham Narayan, J. B. Holberg, Elena Sabbi, A. Calamida et al.
3 citations
Monthly Notices of the Royal Astronomical Society
Spectroscopy and Chemometric Analyses
2.26
article

DAmodel: hierarchical Bayesian modelling of DA white dwarfs for spectrophotometric calibration

Gautham Narayan, J. B. Holberg, Elena Sabbi, A. Calamida, T. Matheson, T. S. Axelrod, Aidan Berres, A. Rest, R. C. Bohlin, Kaisey S. Mandel, Edward W. Olszewski, John MacKenty, I. Hubený, Benjamin M. Boyd, Matthew Grayling, Susana E. Deustua, Abhijit Saha, Aaron Do, C. W. Stubbs, Mai Li
article en
3 citations

Abstract

ABSTRACT We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside three CALSPEC standards, from 912 Å to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zero points and WD parameters (surface gravity $\log g$, effective temperature $T_{\text{eff}}$, extinction $A_V$, dust relation parameter $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic Hubble Space Telescope Wide Field Camera 3 (HST/WFC3) UVIS and IR photometry, HST/STIS UV spectroscopy, and ground-based optical spectroscopy to sub-per cent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables 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 spectral energy distributions from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory’s Legacy Survey of Space and Time and the Nancy Grace Roman Space Telescope.

Monthly Notices of the Royal Astronomical SocietyVol. 540(1)
Space Telescope Science Institute (US), National Institute of Standards and Technology (US), University of Arizona (US), Johns Hopkins University (US), University of Illinois Urbana-Champaign (US), University of Cambridge (GB), Johns Hopkins Medicine (US), Harvard University Press (US), Gemini North Observatory (US), Université Bourgogne Franche-Comté (FR), Institute of Astronomy (RU), Gemini South Observatory (CL), Center for Astrophysics Harvard & Smithsonian (US), National Center for Supercomputing Applications (US), NSF NOIRLab (US), University of Baltimore (US)
National Science Foundation, Smithsonian Institution, U.S. Department of Energy, Harvard University, Michigan State University, Space Telescope Science Institute, University of Cambridge, European Commission, Ministério da Ciência, Tecnologia e Inovação, Korea Astronomy and Space Science Institute, Ministério da Ciência, Tecnologia, Inovações e Comunicações, Agencia Nacional de Investigación y Desarrollo, University of Illinois at Urbana-Champaign, College of Engineering, Michigan State University, University of North Carolina at Chapel Hill, Science and Technology Facilities Council, Christ's College, University of Cambridge, SLAC National Accelerator Laboratory
Openalex Percentile: Top 14%
Spectroscopy and Chemometric Analyses
2.26
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