Speaker
Saeeda Sajjad
(Theoretical Research Institute, Pakistan Academy of Sciences (TRIPAS), Pakistan, Centre for Astro-Particle Physics (CAPP), University of Johannesburg, South Africa)
Description
Machine learning approaches based on the Fermi GBM catalogue data typically use parameters such as the duration of the bursts, its fluence or flux along with various parameters obtained from spectral fits. These approaches typically ignore the error on the spectral parameters. Yet, these errors also contain information about the GRB. In this work, we use the errors on the spectral parameters to simulate spectra compatible with the GRB observed data. We discuss the advantages of using such an approach in comparison with approaches that ignore the error parameters. In particular, we show how this allows us to study GRBs compatible with various sub-ranges in the spectral parameter space.
Author
Saeeda Sajjad
(Theoretical Research Institute, Pakistan Academy of Sciences (TRIPAS), Pakistan, Centre for Astro-Particle Physics (CAPP), University of Johannesburg, South Africa)
Co-authors
Tamador Aldowma
(Johannesburg University, Omdurman Islamic University)
Ms
Adrita Khan
(Center for Computational & Data Sciences, Independent University, Bangladesh)
Soebur Razzaque
(University of Johannesburg)