2016
DOI: 10.1088/1538-3873/128/966/084503
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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… Show more

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Cited by 43 publications
(29 citation statements)
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“…In order to reduce the number of events we need to visually inspect we have developed a prioritizer model based on a trained probabilistic classifier. The use of trained classifier models is becoming a common post-processing technique in FRB surveys (Wagstaff et al 2016) in order to manage the large number of detected events. Our model can be found in the survey git repository 1 .…”
Section: Event Classification Strategymentioning
confidence: 99%
“…In order to reduce the number of events we need to visually inspect we have developed a prioritizer model based on a trained probabilistic classifier. The use of trained classifier models is becoming a common post-processing technique in FRB surveys (Wagstaff et al 2016) in order to manage the large number of detected events. Our model can be found in the survey git repository 1 .…”
Section: Event Classification Strategymentioning
confidence: 99%
“…Radio Galaxy Zoo (Banfield et al 2015)) such as pattern recognition and decision trees (Proctor 2011), source matching and pattern recognition (van Velzen et al 2015) and self-organizing maps (Polsterer et al 2015). The latter is an example of a Machine Learning technique, which has come into increasing use in the recent years (especially in pulsar and transient detection/identification, see (Morello et al 2014;Eatough et al 2010;Bates et al 2012;Wagstaff et al 2016)). …”
Section: Introductionmentioning
confidence: 99%
“…As such, and as our understanding of the expected signal properties has grown, so too has the use of machine-learning-based classifiers of candidate events (e.g. Wagstaff et al 2016;Farah et al 2018;Connor & van Leeuwen 2018). 1 http://frbcat.org/ As FRBs are rare and appear not to repeat (with the notable exception of FRB 121102), being able to confidently verify them is an important issue.…”
Section: Introductionmentioning
confidence: 99%