BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit -- Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs
J.-H. Cao, P. Wang, D. Li, Q.-H. Pan, K. Mao, C.-H. Niu, Y.-K. Zhang,, Q.-Y. Qu, W.-J. Lu, J.-S. Zhang, Y.-H. Zhu, Y.-D. Wang, H.-X. Chen, X.-L., Chen, E. G\"ugercino\u{g}lu, J.-H. Fang, Y. Feng, H. Gao, Y.-F. Huang, J. Li,, C.-C. Miao, C.-W. Tsai, J.-M. Yao, S.-P. You

TL;DR
BASSET is a new algorithm that improves narrow-band FRB detection by enhancing signal-to-noise ratio through frequency pattern analysis, leading to more complete and sensitive pulse detection in existing datasets.
Contribution
The paper introduces BASSET, a novel, adaptive search toolkit that significantly improves narrow-band FRB detection and doubles the known pulse count in a real dataset.
Findings
Discovered 79 additional pulses in FAST data, doubling previous counts.
Enhanced detection sensitivity and SNR for narrow-band pulses.
Provided a parallel-accelerated version of the algorithm for efficient processing.
Abstract
The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrow-band pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET employs a time-frequency correlation analysis to identify and remove the noise involved by the zero-detection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was implemented on the FAST real dataset of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the…
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