Speaker
Description
A supernova is a powerful stellar explosion that mars the death of a star. It occurs when a star can no longer sustain the forces that keep it stable, resulting in a sudden collapse or a thermonuclear runaway, releasing enormous amounts of energy within seconds. These events are categorized mainly as thermonuclear or core-collapse types. After the explosion, the expelled stellar material interacts with the surrounding environment to form a supernova remnant (SNR). This research focuses on a radio supernova remnant, SN 2008iz, in a nearby starburst galaxy, M82, known for its intense star formation and heavy obscuration from dust and gas. The nature of the environment limits observations in optical and X-ray wavelengths. Despite these challenges, radio observations reveal complex behavior in the remnant’s evolution, including an unexpected increase in brightness well after the initial explosion. The study uses high-resolution radio interferometry VLBA data collected over multiple periods to examine the structure and evolution of the supernova remnant’s emission. In this study, we analyze multi-epoch VLBA data to investigate its expansion, spectral properties, and circumstellar interaction. The data will be calibrated, imaged, and aligned consistently across all epochs to measure flux densities, spectral indices, and structural evolution of the remnant. Radio light curves will be constructed from model-fitted and image-based flux measurements, allowing us to characterize the timing, strength, and radial position of the observed re-brightening events. Spectral index graphs will be generated to study how the electron energy distribution evolves. By examining the frequency-dependent absorption in the light curves, we will estimate the density of the circumstellar medium and derive the progenitor’s star mass-loss rate. This approach provides an integrated analysis of the remnant’s spectral evolution, shock–CSM interaction, and progenitor star mass loss with an aim to clarify the physical processes that drive the observed variations.
| Stream | Science or Engineering |
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