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Related Experiment Videos

Unveiling core-collapse supernova progenitors: characterization and physical insights through explainable artificial

Marco Grassia1, Stefano Pio Cosentino2, Giuseppe Mangioni3

  • 1Dipartimento di Ingegneria Elettrica Elettronica e Informatica, University of Catania, Catania, Italy.

Scientific Reports
|July 6, 2026
PubMed
Summary

Machine learning rapidly characterizes core-collapse supernovae (CC-SNe) by analyzing their light curves. This AI framework achieves high accuracy, enabling faster analysis of thousands of stellar explosion events for astrophysics research.

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Area of Science:

  • Astrophysics and Computational Science
  • Stellar Evolution and Supernova Physics

Background:

  • Core-collapse supernovae (CC-SNe) are critical endpoints of massive star evolution, essential for astrophysics, cosmology, and multi-messenger astronomy.
  • Current methods for characterizing CC-SNe are time-consuming (weeks to months) and require extensive human expertise, hindering analysis of increasing observational data.
  • The rapid growth of transient surveys necessitates faster and more efficient supernova characterization techniques.

Purpose of the Study:

  • To develop a machine learning framework for rapid and accurate inference of physical parameters of core-collapse supernovae.
  • To enable the characterization of thousands of CC-SN events efficiently, keeping pace with modern astronomical surveys.
  • To leverage explainable AI to understand which supernova phases are most informative for progenitor property determination.

Main Methods:

  • Developed a deep learning model trained on synthetic light curves generated from astrophysical simulations.
  • The framework infers physical parameters (stellar mass, radius, explosion energy) of CC-SNe.
  • Utilized explainable artificial intelligence (XAI) techniques to analyze model decision-making and identify informative supernova phases.

Main Results:

  • The machine learning framework achieves sub-second computation times on standard hardware for CC-SN parameter inference.
  • The deep learning model demonstrates high accuracy, with errors below 5% for most physical parameters when tested on real observations.
  • XAI techniques identified specific supernova light curve phases crucial for determining progenitor properties.

Conclusions:

  • The presented machine learning framework offers a computationally efficient and accurate solution for characterizing core-collapse supernovae.
  • This approach can handle the large data volumes from current and future transient surveys, accelerating astrophysical research.
  • Insights from XAI can guide future observational strategies for optimizing supernova progenitor studies.