Paper

Do they repeat? Monitoring 36 non-repeating FRBs with FAST

Ho, Simon C -C, Uno, Yuri, Yu-Wei, Lin, Chen, Arthur, Hsu, Tzu-Yin, Eie, Sujin, Chibueze, James O, Hashimoto, Tetsuya, Goto, Tomotsugu, Yi Hang Valerie Wong, Aznam, Murthadza, Wang, Poya, Seong Jin Kim, Wang, Pei, Hisano, Shinnosuke

2025

Overview

The origin of fast radio bursts (FRBs), highly energetic, millisecond-duration radio pulses originating from beyond our galaxy, remains unknown. Observationally, FRBs are classified as non-repeating or repeating, however, this classification is complicated by limited observing time and sensitivity constraints, which may result in some repeating FRBs being misidentified as non-repeating. To address this issue, we adopt both empirical and machine-learning techniques from previous studies to identify candidates that may have been misclassified. We conducted follow-up observations of 36 such candidates, each observed for 10 minutes using the Five-hundred-meter Aperture Spherical Telescope (FAST). No radio bursts exceeding a signal-to-noise ratio of 7 were detected, with a typical 7 sigma fluence limit of ~0.013 Jy ms. We constrain the repetition rates of these sources using two statistical models of FRB occurrence. Combining our FAST non-detections with prior observations, we derive upper limits on the repetition rates of ~\\(10^-2.6\\)-\\(10^-0.22\\) hr\\(^-1\\) under a Poisson process, and ~\\(10^-2.3\\)-\\(10^-0.25\\) hr\\(^-1\\) under a Weibull process. This work presents one of the most stringent upper limits on FRB repetition rates to date, based on a sample size five times larger than those used in previous studies.

• Publisher: Cornell University Library, arXiv.org
• Subject: Fluence / Machine learning / Radio bursts / Radio signals / Repetition / Signal to noise ratio / Statistical analysis / Statistical models