Leveraging neutrino flavor physics for supernova model differentiation

TL;DR

This paper develops a statistical data assimilation method to infer supernova matter density profiles from neutrino flavor measurements, demonstrating potential for distinguishing different profiles in future galactic supernova observations.

Contribution

It introduces an expanded neutrino flavor evolution model incorporating realistic matter profiles from hydrodynamic simulations, enhancing the ability to infer supernova matter density from neutrino data.

Findings

Neutrino flavor measurements can discriminate between different matter density profiles.

The SDA method successfully infers matter profiles from simulated flavor data.

Results suggest potential for future supernova neutrino observations to reveal supernova interior structures.

Abstract

Neutrino flavor evolution is critical for understanding the physics of dense astrophysical regimes, including core-collapse supernovae (CCSN). Powerful numerical integration codes exist for simulating these environments, yet a complete understanding of the inherent nonlinearity of collective neutrino flavor oscillations and how it fits within the overall framework of these simulations remains an open challenge. For this reason, we continue developing statistical data assimilation (SDA) to infer solutions to the flavor field in a CCSN envelope, given simulated measurements far from the source. SDA is an inference paradigm designed to optimize a model with sparse data. Our model consists of neutrino beams emanating from a CCSN and coherently interacting with each other and with a background of other matter particles in one dimension $r$. One model feature of high interest is the distribution of those matter particles as a function of radius $r$, or the "matter potential" $V(r)$ -- as it significantly dictates flavor evolution. In this paper, we expand the model beyond previous incarnations, by replacing the monotonically-decaying analytic form for $V(r)$ we previously used with a more complex -- and more physically plausible -- set of profiles derived from a one-dimensional (spherically symmetric) hydrodynamics simulation of a CCSN explosion. We ask whether the SDA procedure can use simulated flavor measurements at physically accessible locations (i.e. in vacuum) to determine the extent to which different matter density profiles through which the neutrinos propagate in the matter-dominated regime are compatible with these measurements. Within the scope of our small-scale model, we find that the neutrino flavor measurements in the vacuum regime are able to discriminate between different matter profiles, and we discuss implications regarding a future galactic CCSN detection.