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

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.

Published in PASP

References in corpus (4)

• A bright millisecond radio burst of extragalactic origin
• Fast Radio Burst Discovered in the Arecibo Pulsar ALFA Survey
• A fast radio burst in the direction of the Carina dwarf spheroidal galaxy
• The Galactic Position Dependence of Fast Radio Bursts and the Discovery of FRB 011025

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• ALFABURST: A commensal search for Fast Radio Bursts with Arecibo
• 81 New Candidate Fast Radio Bursts in Parkes Archive
• Classifying a frequently repeating fast radio burst, FRB 20201124A, with unsupervised machine learning
• A simulation experiment of a pipeline based on machine learning for neutral hydrogen intensity mapping surveys