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.

9 pages, 8 figures

References in corpus (20)

• QCD Phenomenology based on a Chiral Effective Lagrangian
• Normalizing Flows: An Introduction and Review of Current Methods
• Generic conditions for stable hybrid stars
• Science with the Einstein Telescope: a comparison of different designs
• Quark phases in neutron stars and a "third family" of compact stars as a signature for phase transitions
• A new quark-hadron hybrid equation of state for astrophysics - I. High-mass twin compact stars
• Gravitational-wave parameter estimation with autoregressive neural network flows
• Third family of compact stars within a nonlocal chiral quark model equation of state
• Finite-size effects at the hadron-quark transition and heavy hybrid stars
• Exploring QCD matter in extreme conditions with Machine Learning
• Was GW170817 a canonical neutron star merger? Bayesian analysis with a third family of compact stars
• Characteristics of hybrid compact stars with a sharp hadron-quark interface
• Machine Learning for Anomaly Detection in Particle Physics
• Spanning the full range of neutron star properties within a microscopic description
• Degeneracy in the inference of phase transitions in the neutron star equation of state from gravitational wave data
• Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
• Adapting to noise distribution shifts in flow-based gravitational-wave inference
• Constraining the Reionization History using Bayesian Normalizing Flows
• Detecting dense-matter phase transition signatures in neutron star mass-radius measurements as data anomalies using normalising flows
• Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning

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