Seminar

Abstract

Tidal disruption events (TDEs) occur when a star passes too close to a black
hole. Its tidal field will overpower the self-binding energy of the star, leading to its
partial or total destruction. This releases a significant amount of energy, producing
a transient visible at radio, optical, UV and X-ray wavelengths. The exact source
of this emission and the fate of the captured stellar debris remain uncertain, but
depending on the model, properties such as the black hole and stellar masses can be
derived from the lightcurve shape.
About 150 TDEs have been identified so far. This number is expected to signifi-
cantly increase with the Vera C. Rubin Observatory, the latest cutting-edge ground-
based optical telescope. Its Legacy Survey of Space and Time (LSST) will observe
the entire Southern Sky every three nights for ten years. It will detect millions
of changes in the sky per night, of which only a small fraction will originate from
TDEs. Novel methods will be needed to process this unprecedented influx of data
and improve our understanding of these rare events.
In this seminar, I will start with an overview of the theorised mechanisms behind
TDES and their observed properties, followed by introducing the Rubin Observatory
and LSST, and summarising the current use of machine learning in the field of
astrophysical transients. I will then present my PhD research objectives, including
my plans to use machine learning to identify TDEs in LSST data. Finally, I will
describe my activities and results from the first year, concluding with my plans for
the next year.