Searches for Fast Radio Bursts using Machine Learning

Abstract

Fast Radio bursts (FRBs) are enigmatic astrophysical events with millisecond durations and flux densities in the range 0.1-100 Jy, with the prototype source discovered by Lorimer et al. (2007). Like pulsars, FRBs show the characteristic inverse square sweep in observing frequency due to propagation through an ionized medium. This effect is quantified by the dispersion measure (DM). Unlike pulsars, FRBs have anomalously high DMs, which are consistent with an extragalactic origin. Over 100 FRBs have been published at the time of writing, and 13 have been conclusively identified with host galaxies with spectroscopically determined redshifts in the range 0.003 ≤ z ≤ 0.66. Detection of FRBs requires data at radio frequencies to be de-dispersed at many trial DM values. Incoming radio telescope data are appropriately combined for each DM to form a time series that is then searched using matched filters to find events above a certain signal-to-noise threshold. In the past, diagnostic plots showing these events are most commonly inspected by humans to determine if they are of astrophysical origin. With ongoing FRB surveys producing millions of candidates, machine learning algorithms for candidate classification are now necessary. In this thesis, we present state-of-the-art deep neural networks to classify FRB candidates and events produced by radio frequency interference (RFI). We present 11 deep learning models named FETCH, each with accuracy and recall above 99.5% as determined using a dataset comprising real RFI and pulsar candidates. These algorithms are telescope and frequency agnostic and can correctly classify all FRBs with signal-to-noise ratios above 10 in datasets collected with the Parkes telescope and the Australian Square Kilometre Array Pathfinder (ASKAP). We present the design, deployment, and initial results from the real-time commensal FRB search pipeline at the Robert C. Byrd Green Bank Telescope (GBT) named GREENBURST. The pipeline uses FETCH to winnow down the vast number of false-positive single-pulse candidates that mostly result from RFI. In our observations totaling 276 days so far, we have detected individual pulses from 20 known radio pulsars, which provide excellent verification of the system performance. Although no FRBs have been detected to date, we have used our results to update the analysis of Lawrence et al. (2017) to constrain the FRB all-sky rate to be 1140+200-180 per day above a peak flux density of 1 Jy. We also constrain the source count index α = 0.84 ± 0.06, substantially flatter than expected from a Euclidean distribution