Deducing neutron star equation of state from telescope spectra with machine-learning-derived likelihoods

Journal Article · Tue Dec 12 00:00:00 UTC 2023 · Journal of Cosmology and Astroparticle Physics

DOI: https://doi.org/10.1088/1475-7516/2023/12/022 · OSTI ID:2429793

Farrell, Delaney ^[1]; Baldi, Pierre ^[2]; Ott, Jordan ^[2]; Ghosh, Aishik ^[3]; Steiner, Andrew W. ^[4]; Kavitkar, Atharva ^[5]; Lindblom, Lee ^[6]; Whiteson, Daniel ^[2]; Weber, Fridolin ^[7]

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1. San Diego State Univ., CA (United States)
2. Univ. of California, Irvine, CA (United States)
3. Univ. of California, Irvine, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
4. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
5. Technical University Kaiserslautern (Germany)
6. Univ. of California, San Diego, CA (United States)
7. San Diego State Univ., CA (United States); Univ. of California, San Diego, CA (United States)

The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star's interior is not directly observable, its pressure and density determine the star's macroscopic structure which affects the spectra observed in telescopes. The relationship between the observations and the internal state is complex and partially intractable, presenting difficulties for inference. Previous work has focused on the regression from stellar spectra of parameters describing the internal state. We demonstrate a calculation of the full likelihood of the internal state parameters given observations, accomplished by replacing intractable elements with machine learning models trained on samples of simulated stars. Our machine-learning-derived likelihood allows us to perform maximum a posteriori estimation of the parameters of interest, as well as full scans. We demonstrate the technique by inferring stellar mass and radius from an individual stellar spectrum, as well as equation of state parameters from a set of spectra. Our results are more precise than pure regression models, reducing the width of the parameter residuals by 11.8% in the most realistic scenario. The neural networks will be released as a tool for fast simulation of neutron star properties and observed spectra.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Nuclear Physics (NP)
• Grant/Contract Number: AC02-05CH11231; AC05-00OR22725; SC0009920
• OSTI ID: 2429793
• Journal Information: Journal of Cosmology and Astroparticle Physics, Journal Name: Journal of Cosmology and Astroparticle Physics Journal Issue: 12 Vol. 2023; ISSN 1475-7516
• Publisher: Institute of Physics (IOP)Copyright Statement
• Country of Publication: United States
• Language: English