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.

arXiv commentPublished in PASP

References in corpus (22)

• The ATNF Pulsar Catalogue
• A bright millisecond radio burst of extragalactic origin
• A Population of Fast Radio Bursts at Cosmological Distances
• Fast Radio Burst Discovered in the Arecibo Pulsar ALFA Survey
• DiFX2: A more flexible, efficient, robust and powerful software correlator
• Dense magnetized plasma associated with a fast radio burst
• Fast radio bursts: the last sign of supramassive neutron stars
• Cosmological Fast Radio Bursts from Binary Neutron Star Mergers
• A real-time fast radio burst: polarization detection and multiwavelength follow-up
• A possible connection between Fast Radio Bursts and Gamma-Ray Bursts
• On the origin of a highly-dispersed coherent radio burst
• Cosmological Fast Radio Bursts from Binary White Dwarf Mergers
• 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
• Radio Bursts with Extragalactic Spectral Characteristics Show Terrestrial Origins
• Fast radio bursts may originate from nearby flaring stars
• Identifying the source of perytons at the Parkes radio telescope
• V-FASTR: The VLBA Fast Radio Transients Experiment
• The Allen Telescope Array Fly's Eye Survey for Fast Radio Transients
• Detection of fast radio transients with multiple stations: a case study using the Very Long Baseline Array
• Limits on the event rates of fast radio transients from the V-FASTR experiment
• A framework for interpreting fast radio transients search experiments: application to the V-FASTR experiment

Cited by in corpus (21)

• Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach
• Classifying Radio Galaxies with Convolutional Neural Network
• FETCH: A deep-learning based classifier for fast transient classification
• Applying Deep Learning to Fast Radio Burst Classification
• Separation of pulsar signals from noise with supervised machine learning algorithms
• Five new real-time detections of Fast Radio Bursts with UTMOST
• Uncloaking hidden repeating fast radio bursts with unsupervised machine learning
• Machine learning classification of CHIME fast radio bursts -- I. Supervised methods
• Machine learning classification of CHIME fast radio bursts: II. Unsupervised Methods
• A Synoptic VLBI Technique for Localizing Non-Repeating Fast Radio Bursts with CHIME/FRB
• Single Pulse Detection Algorithms for Real-time Fast Radio Burst Searches using GPUs
• ALFABURST: A commensal search for Fast Radio Bursts with Arecibo
• The High Cadence Transient Survey (HITS): Compilation and characterization of light-curve catalogs
• 81 New Candidate Fast Radio Bursts in Parkes Archive
• Verifying and Reporting Fast Radio Bursts
• Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations
• Classifying a frequently repeating fast radio burst, FRB 20201124A, with unsupervised machine learning
• Machine Learning Classification of Repeating FRBs from FRB121102
• Black Swans in Astronomical Data
• Search for fast radio transients using Arecibo drift-scan observations at 1.4 GHz
• A simulation experiment of a pipeline based on machine learning for neutral hydrogen intensity mapping surveys