| Name: | Ajay Kumar |
| Affiliation: | National centre for Radio Astrophysics |
| Conference ID: | ASI2025_737 |
| Title: | Probing Fast Radio Bursts using Deep Learning and GMRT |
| Authors: | Ajay Kumar, Yogesh Maan, Yash Bhusare, Shriharsh Tendulkar, Visweshawar Ram Marthi |
| Authors Affiliation: | National centre for Radio Astrophysics, Pune |
| Mode of Presentation: | Oral |
| Abstract Category: | High Energy Phenomena, Fundamental Physics and Astronomy |
| 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. The results obtained from the application
of Frabjous on simulated and first CHIME/FRB catalogue and the potential
for more accurate and reliable classification will be discussed.
I will also present the results from recent observational campaigns of
several active repeaters, including FRB220912A and FRB240114A using
uGMRT. I will describe their burst properties, energy distributions at
lower radio frequencies, host environments, temporal evolution, and
frequency-dependent activity. Finally, I will discuss the implications
of our results in the context of proposed progenitors models and
emission mechanisms for repeating FRBs. |
