GRB Optical and X-ray Plateau Properties Classifier Using Unsupervised Machine Learning
1 Aug 2023·,,,
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S. bhardwaj
M.g. dainotti
T.s.s. venkatesh
A. narendra
Anish Kalsi
E. rinaldi
A. pollo
GMM clustering of X-ray GRB plateau properties (MNRAS 525, 2023)Abstract
We apply unsupervised machine learning techniques to classify Gamma-Ray Burst (GRB) afterglows based
on their optical and X-ray plateau emission properties. By analysing a large sample of GRBs, we identify
distinct classes of plateau behaviour that may reflect different physical mechanisms or progenitor
populations. Our classification scheme provides a data-driven framework for understanding the diversity
of GRB afterglow properties.
Type
Publication
Monthly Notices of the Royal Astronomical Society, Volume 525, Issue 4, Pages 5204–5223
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Anish Kalsi
(he/him)
Astronomy PhD Student
I am an Astrophysics PhD Candidate at the Nicolaus Copernicus Astronomical Center (NCAC PAS) in Poland,
currently bridging the gap between theoretical models and observational data.
With a background in Engineering Physics from Delhi Technological University and an MS in Astrophysics & Cosmology from the University of Padova,
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