JOURNAL ARTICLE

Predicting Physical Parameters of Cepheid and RR Lyrae variables in an Instant with Machine Learning.

  • Published In: Proceedings of the International Astronomical Union, 2023, v. 19, n. S368. P. 104 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bhardwaj, A.; Bellinger, E. P.; Kanbur, S. M.; Marconi, M. 3 of 3

Abstract

We present a machine learning method to estimate the physical parameters of classical pulsating stars such as RR Lyrae and Cepheid variables based on an automated comparison of their theoretical and observed light curve parameters at multiple wavelengths. We train artificial neural networks (ANNs) on theoretical pulsation models to predict the fundamental parameters (mass, radius, luminosity, and effective temperature) of Cepheid and RR Lyrae stars based on their period and light-curve parameters. The fundamental parameters of these stars can be estimated up to 60 percent more accurately when the light-curve parameters are taken into consideration. This method was applied to the observations of hundreds of Cepheids and thousands of RR Lyrae in the Magellanic Clouds to produce catalogs of estimated masses, radii, luminosities, and other parameters of these stars. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Proceedings of the International Astronomical Union. 2023/08, Vol. 19, Issue S368, p104
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:1743-9213
  • DOI:10.1017/S1743921323000571
  • Accession Number:187089464
  • Copyright Statement:Copyright of Proceedings of the International Astronomical Union is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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