Charles E. Kaufman Foundation

2022 New Investigator Grant

V. Ashley Villar, Ph.D.

Unveiling the final days of stellar life through exotic explosions


Abstract

Massive stars die as supernovae, which are stellar explosions that enrich our cosmos with heavy elements. In rare cases, a star lets out a “death throe,” an eruptive mass loss that occurs for months to years preceding the supernova. As a result, the star is cocooned in a sheath of material that is shock-heated once the star goes supernova. We still do not understand why some stars experience this mass loss while the majority do not. Yet these rare supernovae, known as Type IIn supernovae, illuminate massive gaps in our fundamental understanding of stellar evolution. The observational solution to this decades-old problem is on the horizon. A new survey, the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, will drastically increase the number of observed Type IIn supernovae, from dozens of events to 25,000 events every year. This proposal brings together expertise in high energy physics and machine learning/statistics in order to illuminate the final tumultuous years of stars’ lives. We will conduct a statistical study of the largest sample (more than 150) of Type IIn supernovae, modeling their broadband light as a function of time. This will result, for the first time, in a statistical understanding of the energetics of the supernova explosions as well as the geometry of the mass loss. This will be used to directly test the nature of the death throes foreshadowing the supernovae. Furthermore, researchers will prepare the field for a new revolution of Big Data in supernova science by creating open source pipelines to rapidly accelerate traditional model fitting using neural networks. The result of this research will be the largest and first physical population study of Type IIn supernovae, which will illuminate the final days of eruptive massive stars. It will also serve as a Rosetta Stone for machine learning-enabled statistical inference.

 

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