Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach

• Published in: arXiv.org
• Main Authors: Zhang, Yunfan Gerry, Gajjar, Vishal, Griffin, Foster, Siemion, Andrew, Cordes, James, Law, Casey, Wang, Yu
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 09.09.2018
• Subjects: Algorithms; Arrivals; Artificial neural networks; C band; Data points; Fluence; Machine learning; Neural networks; Periodic variations; Physics - High Energy Astrophysical Phenomena; Physics - Instrumentation and Methods for Astrophysics; Radio bursts; Time dependence
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1809.03043

We report the detection of 72 new pulses from the repeating fast radio burst FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green Bank Telescope. The new pulses were found with a convolutional neural network in data taken on August 26, 2017, where 21 bursts have been previously detected. Our technique combines neural network detection with dedispersion verification. For the current application we demonstrate its advantage over a traditional brute-force dedis- persion algorithm in terms of higher sensitivity, lower false positive rates, and faster computational speed. Together with the 21 previously reported pulses, this observa- tion marks the highest number of FRB 121102 pulses from a single observation, total- ing 93 pulses in five hours, including 45 pulses within the first 30 minutes. The number of data points reveal trends in pulse fluence, pulse detection rate, and pulse frequency structure. We introduce a new periodicity search technique, based on the Rayleigh test, to analyze the time of arrivals, with which we exclude with 99% confidence pe- riodicity in time of arrivals with periods larger than 5.1 times the model-dependent time-stamp uncertainty. In particular, we rule out constant periods >10 ms in the barycentric arrival times, though intrinsic periodicity in the time of emission remains plausible.