Probing Fast Radio Bursts Using Deep Learning and GMRT

Abstract:
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.