Searches for Fast Radio Bursts using Machine Learning

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 of standard candles (where α =1.5). We make predictions for detection rates with GREENBURST as well as other ongoing and planned FRB experiments. Lastly, we present the discovery of FRB 180417 through a targeted search for faint FRBs near the core of the Virgo cluster using ASKAP. Several radio telescopes promptly followed up the FRB for a total of 27 h, but no repeat bursts were detected. An optical follow-up of FRB 180417 using the PROMPT5 telescope revealed no new sources down to an R-band magnitude of 20.1. We argue that FRB 180417 is likely behind the Virgo cluster as the Galactic and intracluster DM contributions are small compared to the DM of the FRB, and there are no galaxies in the line of sight. Adopting an FRB rate of 103 FRBs sky-1day-1 with flux above 1 Jy out to z=1, our non-detection of FRBs from Virgo constrains (at 68 % confidence limit) the faint-end slope of the luminosity function Lmin ≥ 6.5 × 1039 erg s-1

GBTrans: A commensal search for radio pulses with the Green Bank twenty metre telescope

We describe GBTrans, a real-time search system designed to find fast radio bursts (FRBs) using the 20-m radio telescope at the Green Bank Observatory. The telescope has been part of the Skynet educational program since 2015. We give details of the observing system and report on the non-detection of FRBs from a total observing time of 503 days. Single pulses from four known pulsars were detected as part of the commensal observing. The system is sensitive enough to detect approximately half of all currently known FRBs and we estimate that our survey probed redshifts out to about 0.3 corresponding to an effective survey volume of around 124,000~Mpc$^3$. Modeling the FRB rate as a function of fluence, $F$, as a power law with $F^{-\alpha}$, we constrain the index $\alpha < 2.5$ at the 90% confidence level. We discuss the implications of this result in the context of constraints from other FRB surveys.Comment: 7 pages, 6 figure

On Adversarial Robustness: A Neural Architecture Search perspective

Adversarial robustness of deep learning models has gained much traction in the last few years. Various attacks and defenses are proposed to improve the adversarial robustness of modern-day deep learning architectures. While all these approaches help improve the robustness, one promising direction for improving adversarial robustness is unexplored, i.e., the complex topology of the neural network architecture. In this work, we address the following question: "Can the complex topology of a neural network give adversarial robustness without any form of adversarial training?". We answer this empirically by experimenting with different hand-crafted and NAS-based architectures. Our findings show that, for small-scale attacks, NAS-based architectures are more robust for small-scale datasets and simple tasks than hand-crafted architectures. However, as the size of the dataset or the complexity of task increases, hand-crafted architectures are more robust than NAS-based architectures. Our work is the first large-scale study to understand adversarial robustness purely from an architectural perspective. Our study shows that random sampling in the search space of DARTS (a popular NAS method) with simple ensembling can improve the robustness to PGD attack by nearly 12%. We show that NAS, which is popular for achieving SoTA accuracy, can provide adversarial accuracy as a free add-on without any form of adversarial training. Our results show that leveraging the search space of NAS methods with methods like ensembles can be an excellent way to achieve adversarial robustness without any form of adversarial training. We also introduce a metric that can be used to calculate the trade-off between clean accuracy and adversarial robustness. Code and pre-trained models will be made available at https://github.com/tdchaitanya/nas-robustness © 2021 IEEE