DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Boyd, Benjamin M. Narayan, Gautham Mandel, Kaisey S. Grayling, Matthew Saha, Abhijit Axelrod, Tim Matheson, Thomas Olszewski, Edward W. Calamida, Annalisa Do, Aaron Bohlin, Ralph C. Holberg, Jay B. Hubeny, Ivan Deustua, Susana Rest, Armin Stubbs, Christopher W. Berres, Aidan Li, Mai Mackenty, John W. Sabbi, Elena |
| author_facet | Boyd, Benjamin M. Narayan, Gautham Mandel, Kaisey S. Grayling, Matthew Saha, Abhijit Axelrod, Tim Matheson, Thomas Olszewski, Edward W. Calamida, Annalisa Do, Aaron Bohlin, Ralph C. Holberg, Jay B. Hubeny, Ivan Deustua, Susana Rest, Armin Stubbs, Christopher W. Berres, Aidan Li, Mai Mackenty, John W. Sabbi, Elena |
| contents | 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 $μ$m. The framework is the first of its kind to jointly infer photometric zeropoints 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-percent 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08809 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration Boyd, Benjamin M. Narayan, Gautham Mandel, Kaisey S. Grayling, Matthew Saha, Abhijit Axelrod, Tim Matheson, Thomas Olszewski, Edward W. Calamida, Annalisa Do, Aaron Bohlin, Ralph C. Holberg, Jay B. Hubeny, Ivan Deustua, Susana Rest, Armin Stubbs, Christopher W. Berres, Aidan Li, Mai Mackenty, John W. Sabbi, Elena Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Solar and Stellar Astrophysics Applications 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 $μ$m. The framework is the first of its kind to jointly infer photometric zeropoints 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-percent 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. |
| title | DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Solar and Stellar Astrophysics Applications |
| url | https://arxiv.org/abs/2412.08809 |