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Gamma-ray bursts (GRBs) are one of the most luminous transient astrophysical phenomena in the Universe, with isotropic equivalent energies reaching up to 10^54 ergs. GRB prompt emission spectra typically span the gamma-ray energy range from keV to MeV, exhibiting durations that range from milliseconds to several minutes. GRBs have been detected at cosmological redshifts over 9, offering a window to probe the early universe.
Despite several decades of intense observational and theoretical study, fundamental questions regarding the emission mechanisms, progenitor systems, central engines, relativistic jet launching mechanisms, and physical processes governing GRBs remain open challenges. A central yet unsolved problem in GRB research is the classification of these energetic explosions. Different classification schemes based on properties such as duration, fluence, spectral lags, afterglow characteristics, host galaxy types and locations, and other features have been proposed. However, it remains unclear how effectively these classification systems correlate with intrinsically distinct classes of GRB progenitors and central engines. This thesis investigates and critically evaluates various GRB classification methods using a multi-wavelength dataset encompassing observations spanning gamma-ray, X-ray, and optical wavelengths. We also utilised the machine learning algorithms to disentangle various classes of GRBs and to better understand their underlying physical properties. 
Future gravitational wave observations will play a crucial role in advancing our understanding of GRBs. The detection of gravitational waves from binary neutron star mergers has opened up a new window to study these systems and their connection to GRBs. By combining gravitational wave observations with electromagnetic observations across the spectrum in the near future, we can gain unprecedented insights into the nature of these enigmatic phenomena.
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Dimple is currently a PhD student at ARIES.

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