Detecting the third family of compact stars with normalizing flows
Abstract: We explore the anomaly detection framework based on Normalizing Flows (NF) models introduced in \cite{PhysRevC.106.065802} to detect the presence of a large (destabilising) dense matter phase transition in neutron star (NS) observations of masses and radii, and relate the feasibility of detection with parameters of the underlying mass-radius sequence, which is a functional of the dense matter equation of state. Once trained on simulated data featuring continuous $M(R)$ solutions (i.e., no phase transitions), NF is used to determine the likelihood of a first-order phase transition in a given set of $M(R)$ observations featuring a discontinuity, i.e., perform the anomaly detection. Different mock test sets, featuring two branch solutions in the $M(R)$ diagram, were parameterized by the NS mass at which the phase transition occurs, $M_c$, and the radius difference between the heaviest hadronic star and lightest hybrid star, $\Delta R$. We analyze the impact of these parameters on the NF performance in detecting the presence of a first-order phase transition. Among the results, we report that given a set of 15 stars with radius uncertainty of $0.2$ km, a detection of a two-branch solution is possible with 95\% accuracy if $\Delta R > 0.4$ km.
- F. Morawski and M. Bejger, Phys. Rev. C 106, 065802 (2022).
- M. G. Alford and S. Han, Eur. Phys. J. A 52, 62 (2016), arXiv:1508.01261 [nucl-th] .
- D. J. Rezende and S. Mohamed, “Variational inference with normalizing flows,” (2016), arXiv:1505.05770 [stat.ML] .
- G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing flows for probabilistic modeling and inference,” (2021), arXiv:1912.02762 [stat.ML] .
- C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, “nflows: normalizing flows in pytorch,” (2020).
- M. Branchesi et al., JCAP 07, 068 (2023), arXiv:2303.15923 [gr-qc] .
- M. Evans et al., (2021), arXiv:2109.09882 [astro-ph.IM] .
- D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” (2018), arXiv:1807.03039 [stat.ML] .
- D. P. Kingma and J. Ba, arXiv preprint arXiv:1412.6980 (2014).
- M. Majnik and Z. Bosnić, Intelligent data analysis 17, 531 (2013).
- A. P. Bradley, Pattern Recognition 30, 1145 (1997).
- S. Typel and H. H. Wolter, Nucl. Phys. A 656, 331 (1999).
- T. Hatsuda and T. Kunihiro, Phys. Rept. 247, 221 (1994), arXiv:hep-ph/9401310 .
- C. A. Raithel and E. R. Most, Phys. Rev. Lett. 130, 201403 (2023), arXiv:2208.04294 [astro-ph.HE] .
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