Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves

TL;DR

This study enhances machine-learning methods for classifying the dense-matter equation of state from supernova gravitational waves by incorporating realistic noise, multiple models, and core bounce time uncertainty, without degrading accuracy.

Contribution

It extends previous work by including realistic noise, multiple progenitor models, and bounce time uncertainty, improving the robustness of EOS classification.

Findings

Inclusion of noise and multiple models does not reduce classification accuracy.

Larger datasets from diverse models improve training and accuracy.

Realistic conditions can be incorporated without sacrificing performance.

Abstract

Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, we relax three key assumptions. First, we include real detector noise. Second, we expand the analysis from a single progenitor model to four models spanning 12 to 40 solar masses, and for each mass we consider multiple rotational configurations, from slow to rapid. Third, we introduce uncertainty in the core bounce time of up to 20 ms, rather than assuming it is known precisely. We find that none of these effects significantly degrades EOS classification performance. Instead, the larger dataset associated with multiple progenitor models and noise realizations improves training and classification accuracy. This study is a step in a broader effort to progressively incorporate more realistic conditions into gravitational-wave inference for core-collapse supernovae.