Work

Core-collapse supernova simulations and collective neutrino flavor conversions: current status and prospects

Year: 2026

Type: article

Abstract: In this review, we survey neutrino quantum kinetics in core-collapse supernovae (CCSNe), with emphasis on collective flavor conversion and its possible role in the explosion dynamics. We summarize the physical ingredients of modern CCSN simulations, with particular emphasis on neutrino transport and the detailed neutrino distributions relevant for flavor evolution. We then discuss the theoretical framework of collective neutrino flavor instabilities and conversions, together with insights from analytical and numerical studies. We also review ongoing efforts to incorporate flavor conversion into global CCSN models and the challenges of linking microscopic quantum kinetic processes with macroscopic dynamics. We briefly discuss how collective flavor conversion may affect the interpretation of the SN 1987A neutrino signal and of a future Galactic event. We conclude by outlining future directions toward a self-consistent treatment of neutrino flavor evolution in CCSN simulations.

Source: Classical and Quantum Gravity

DOI: 10.1088/1361-6382/ae9f10

Authors: Jiabao Liu, Akira Harada, Hiroki Nagakura

Institutions: Waseda University, RIKEN, Ibaraki National College of Technology, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences, National Astronomical Observatory of Japan

Topic: Neutrino Physics Research

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,325

Funders: Japan Society for the Promotion of Science, National Institutes of Natural Sciences, Institute for Cosmic Ray Research, University of Tokyo

Awards: ニュートリノ輻射流体計算を用いた超新星爆発及び連星中性子合体の包括的研究, The fate of massive stars explored by the first principle calculations in general relativity with nuclear physics, 重力崩壊型超新星における集団的ニュートリノフレーバー変換の役割, G8479122366, Accurate and Systematic Theoretical Modeling of Core-collapse Supernovae via Machine Learning Technique