From the Early Slope to Curvature: Future Prospects and challenges for Astrophysical Parameter Estimation Using the Core-Collapse Supernova High-Frequency Feature

Abstract

Gravitational-wave (GW) signals from core-collapse supernovae (CCSNe) contain both stochastic and deterministic components. Among these, the High-Frequency Feature (HFF), associated with PNS oscillations, has emerged as a robust observable for probing dense-matter physics. Previous studies of the HFF have primarily focused on the early-time slope as a diagnostic of PNS contraction and its dependence on the equation of state (EOS). In this work, we extend the analysis beyond the early-time linear regime. In particular, we discuss how higher-order features of the HFF evolution, such as its curvature, may encode additional information about the time-dependent structure of the PNS and the transition between oscillation modes. Using real interferometric noise and reconstruction techniques on detected candidates by the coherent WaveBurst (cWB) algorithm in its cWB-XP implementation, we illustrate how such features can be accessed. By comparing reconstructed signals at different source distances, we assess the relative impact of detector noise.