Probing Fast Radio Bursts Using Deep Learning and GMRT

Fast Radio bursts are bright dispersed radio pulses of cosmological origin. Currently, several hundred of them are known and published. The population of FRBs are classified as one-off events and repeaters. A small fraction of FRBs are active repeaters, which can be studied in great detail to gain insights into their origins and emission mechanism. The discovery rate of FRBs is already a few per day and is expected to increase rapidly with new surveys coming online. The growing number of events necessitates prioritized follow-up due to limited multi-wavelength resources.

I will describe Frabjous, a deep learning framework for an automated morphology classifier, with an aim towards enabling the rapid follow-up of anomalous and intriguing FRBs and a comprehensive statistical analysis of FRB morphologies. I will then discuss the results obtained from the application of Frabjous on simulated and first CHIME/FRB catalog and the potential for more accurate and reliable classification.

I will also present the results from recent observational campaigns of several active repeaters, including FRB 20220912A, FRB 20240114A and FRB 20240619D using uGMRT. Furthermore, I will describe their burst properties, energy distributions, host environments, and temporal evolution of burst rates at lower radio frequencies. Finally, I will discuss the implications of our results in the context of proposed progenitors models and emission mechanisms for repeating FRBs.

Organiser