Frabjous: Deep Learning Fast Radio Burst Morphologies

TL;DR

Frabjous is a deep learning framework designed to classify FRB morphologies automatically, aiding rapid follow-up and statistical analysis, despite current accuracy limitations due to limited training data.

Contribution

This paper introduces a novel simulation-based training approach for FRB morphology classification using deep learning, addressing data scarcity issues.

Findings

Achieved approximately 55% classification accuracy on CHIME/FRB data.

Outperformed random chance with 20% accuracy across five classes.

Discussed limitations and future directions for improving FRB classification.

Abstract

The increasing field of view of radio telescopes and improved data processing capabilities have led to a surge in the detection of Fast Radio Bursts (FRBs). 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, requiring rapid and automated classification. In this study, we introduce Frabjous, a deep learning framework for an automated morphology classifier with an aim towards enabling the prompt follow-up of anomalous and intriguing FRBs, and a comprehensive statistical analysis of FRB morphologies. Deep learning models require a large training set of each FRB archetype, however, publicly available data lacks sufficient samples for most FRB types. In this paper, we build a simulation framework for generating realistic examples of FRBs and train a network based on a combination of simulated and real data, starting with the CHIME/FRB catalog. Applying our framework to the first CHIME/FRB catalog, we achieve an overall classification accuracy of approximately 55%, well over a random multiclass classification rate of 20 % with five balanced classes during training. While this falls short of desirable performance, we critically discuss the limitations of our approach and propose potential avenues for improvement. Future work should explore strategies to augment training datasets and broaden the scope of FRB morphological studies, aiming for more accurate and reliable classification results.