Asteroseismology of δ Scuti stars: emulating model grids using a neural network
Article
Scutt, Owen J., Murphy, Simon J, Nielsen, Martin B, Davies, Guy R, Bedding, Timothy R and Lyttle, Alexander J. 2023. "Asteroseismology of δ Scuti stars: emulating model grids using a neural network
." Monthly Notices of the Royal Astronomical Society. 525 (4), pp. 5235-5244. https://doi.org/10.1093/mnras/stad2621
Article Title | Asteroseismology of δ Scuti stars: emulating model grids using a neural network |
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ERA Journal ID | 1074 |
Article Category | Article |
Authors | Scutt, Owen J., Murphy, Simon J, Nielsen, Martin B, Davies, Guy R, Bedding, Timothy R and Lyttle, Alexander J |
Journal Title | Monthly Notices of the Royal Astronomical Society |
Journal Citation | 525 (4), pp. 5235-5244 |
Number of Pages | 10 |
Year | 2023 |
Publisher | Oxford University Press |
Place of Publication | United Kingdom |
ISSN | 0035-8711 |
1365-2966 | |
Digital Object Identifier (DOI) | https://doi.org/10.1093/mnras/stad2621 |
Web Address (URL) | https://academic.oup.com/mnras/article/525/4/5235/7258825 |
Abstract | Young δ Scuti (Sct) stars have proven to be valuable asteroseismic targets, but obtaining robust uncertainties on their inferred properties is challenging. We aim to quantify the random uncertainties in grid-based modelling of δ Sct stars. We apply Bayesian inference using nested sampling and a neural network emulator of stellar models, testing our method on both simulated and real stars. Based on results from simulated stars, we demonstrate that our method can recover plausible posterior probability density estimates while accounting for both the random uncertainty from the observations and neural network emulation. We find that the posterior distributions of the fundamental parameters can be significantly non-Gaussian and multimodal, and have strong covariance. We conclude that our method reliably estimates the random uncertainty in the modelling of δ Sct stars and paves the way for the investigation and quantification of the systematic uncertainty. |
Keywords | asteroseismology |
ANZSRC Field of Research 2020 | 510109. Stellar astronomy and planetary systems |
Byline Affiliations | University of Birmingham, United Kingdom |
Centre for Astrophysics | |
University of Sydney |
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https://research.usq.edu.au/item/z26w5/asteroseismology-of-scuti-stars-emulating-model-grids-using-a-neural-network
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