
ANRF ARG Project
Stars Inside Out: Exploring Stellar Stochasticity and Its Imprint on Cosmic Dust
This project explores the connection between stellar evolution and stellar environments, and between stellar environments and stardust. We focus on the large uncertainties in stellar evolution, particularly those related to mass loss, chemical yields, lifetimes, and final fates in supernovae. In this work, we study various supernova subtypes and their evolutionary pathways. The physics and chemistry of stars and the interstellar medium are the main focus, where cosmic dust acts as the connecting link. Overall, we combine observational properties across X-ray, UV, optical, IR, and radio wavelengths that are relevant to supernovae and the evolution of their dusty remnants. By combining multi-wavelength observations with theoretical models, we aim to build a coherent picture of how stars shape their environments throughout their lifetimes. This approach helps us understand how stellar evolution ultimately leads to dust formation and chemical enrichment in galaxies.
The project is theoretical and computational in nature, making use of several advanced computational techniques. In parallel, the study is associated with our ongoing James Webb Space Telescope (JWST) programs to detect dust in a large sample of supernovae, addressing the same questions from an observational perspective. The project syncs with the large transient survey Young Supernova Experiment (YSE), as part of the Pan-STARRS transient program, designed to detect and classify supernovae of diverse properties. The newly started LSST (Legacy Survey of Space and Time) surveys are expected to regularly detect numerous supernovae, whose properties will be accounted for by this study.
Key focus
Stellar evolution, supernova explosion, Cosmic dust, Chemical evolution of galaxies, JWST, Machine learning
Open position (to be filled by Aug 2026)

(1) PhD position at the Indian Institute of Astrophysics
(2) Senior Project Scientist at the Indian Institute of Astrophysics