ABSTRACT We conducted a drift-scan observation campaign using the 305-m Arecibo telescope in 2020 January and March when the observatory was temporarily closed during the intense earthquakes and the initial outbreak of the COVID-19 pandemic, respectively. The primary objective of the survey was to search for fast radio transients, including fast radio bursts (FRBs) and rotating radio transients (RRATs). We used the seven-beam ALFA receiver to observe different sections of the sky within the declination region ∼(10°–20°) on 23 nights and collected 160 h of data in total. We searched our data for single-pulse transients, of covering up to a maximum dispersion measure of 11 000 pc cm−3 at which the dispersion delay across the entire bandwidth is equal to the 13-s transit length of our observations. The analysis produced more than 18 million candidates. Machine learning techniques sorted the radio frequency interference and possibly astrophysical candidates, allowing us to visually inspect and confirm the candidate transients. We found no evidence for new astrophysical transients in our data. We also searched for emission from repeated transient signals, but found no evidence for such sources. We detected single pulses from two known pulsars in our observations and their measured flux densities are consistent with the expected values. Based on our observations and sensitivity, we estimated the upper limit for the FRB rate to be <2.8 × 105 sky−1 d−1 above a fluence of 0.16 Jy ms at 1.4 GHz, which is consistent with the rates from other telescopes and surveys.
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FETCH: A deep-learning based classifier for fast transient classification
ABSTRACT With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending real-time triggers for prompt follow-ups with other instruments. In this paper, we have used the technique of Transfer Learning to train the state-of-the-art deep neural networks for classification of FRB and Radio Frequency Interference (RFI) candidates. These are convolutional neural networks which work on radio frequency-time and dispersion measure-time images as the inputs. We trained these networks using simulated FRBs and real RFI candidates from telescopes at the Green Bank Observatory. We present 11 deep learning models, each with an accuracy and recall above 99.5 per cent on our test data set comprising of real RFI and pulsar candidates. As we demonstrate, these algorithms are telescope and frequency agnostic and are able to detect all FRBs with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide an open-source python package fetch (Fast Extragalactic Transient Candidate Hunter) for classification of candidates, using our models. Using fetch, these models can be deployed along with any commensal search pipeline for real-time candidate classification.
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- Award ID(s):
- 1714897
- NSF-PAR ID:
- 10191190
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 497
- Issue:
- 2
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- 1661 to 1674
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/mnras/staa1856
