A Core-Collapse Supernova Neutrino Parameterization with Enhanced Physical Interpretability

TL;DR

This paper presents a new, physically motivated parameterization of supernova neutrino spectra that effectively interprets spectral features, constrains progenitor properties, and links neutrino emission to explosion dynamics and gravitational waves.

Contribution

It introduces a novel parameterization with clear physical meaning, demonstrating its effectiveness on historical data and simulations, and revealing correlations with explosion outcomes and gravitational waves.

Findings

The parameterization fits SN1987A data with statistical significance.

Temporal evolution of τ(t) distinguishes successful from failed explosions.

τ(t) correlates strongly with gravitational-wave signals.

Abstract

We introduce a novel parameterization of supernova neutrino energy spectra with a clear physical motivation. Its central parameter, $\tau(t)$, quantifies the characteristic thermal-diffusion area during the explosion. When applied to the historic SN1987A data, this parameterization yields statistically significant fits and provides robust constraints on the unobserved low-energy portion of the spectrum. Beyond this specific application, we demonstrate the model's power on a suite of 3D core-collapse supernova simulations, finding that the temporal evolution of $\tau(t)$ distinctly separates successful from failed explosions. Furthermore, we constrain the progenitor mass of SN 1987A to approximately 19 solar masses by applying Smoothed Isotonic Regression, while noting the sensitivity of this estimate to observational uncertainties. Moreover, in these simulations, $\tau(t)$ and the gravitational-wave strain amplitude display a strong, synergistic co-evolution, directly linking the engine's energetic evolution to its geometric asymmetry. This implies that the thermodynamic state of the explosion is imprinted not only on the escaping neutrino flux, but also recorded in the shape of the energy spectrum. Our framework therefore offers a valuable tool for decoding the detailed core dynamics and multi-messenger processes of future galactic supernovae.