A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA

TL;DR

This paper presents a machine learning classifier integrated into the V-FASTR system to efficiently identify fast radio bursts in VLBA data, significantly reducing review time and increasing detection accuracy.

Contribution

The paper introduces a novel machine learning classifier that filters 80-90% of candidates with over 98% accuracy, improving the efficiency of FRB detection in radio astronomy data.

Findings

Classifier classifies 80-90% of candidates

Accuracy exceeds 98%

Review time decreased and interesting candidates increased

Abstract

Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts (FRBs) within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80-90% of the candidates, with an accuracy greater than 98%, leaving only the 10-20% most promising candidates to be reviewed by humans.