Reprocessing of a Green Bank 43-meter Telescope Survey of Unidentified Bright Radio Sources for Pulsars and Radio Bursts

TL;DR

This study reprocessed Green Bank 43-m telescope data of 75 bright, unidentified radio sources using deep learning to detect pulsars and radio bursts, but found no new signals.

Contribution

It introduces a deep learning-based reanalysis method for pulsar and burst detection in existing survey data, extending sensitivity to fast, highly dispersed signals.

Findings

No new pulsars or radio bursts detected.

Enhanced sensitivity to sub-millisecond pulsars and fast radio bursts.

Demonstrates the effectiveness of deep learning in reanalyzing radio survey data.

Abstract

We have reprocessed a set of observations of 75 bright, unidentified, steep-spectrum polarized radio sources taken with the Green Bank 43-m telescope to find previously undetected sub-millisecond pulsars and radio bursts. The (null) results of the first search of these data were reported by Schmidt et al. Our reprocessing searched for single pulses out to a dispersion measure (DM) of 1000 pc cm$^{-3}$ which were classified using the Deep Learning based classifier FETCH. We also searched for periodicities at a wider range of DMs and accelerations. Our search was sensitive to highly accelerated, rapidly rotating pulsars (including sub-millisecond pulsars) in compact binary systems as well as to highly-dispersed impulsive signals, such as fast radio bursts. No pulsars or astrophysical burst signals were found in the reprocessing.