17 citations · 21 across the 8 of their papers we have counts for
9 papers
Identifying Merger-Driven and Collapsar-Driven Gamma-Ray Bursts with Precursor based Solely on Prompt Emission
Si-Yuan Zhu, Pak-Hin Thomas Tam, Fu-Wen Zhang +2
Gamma-ray bursts (GRBs) are generally classified as Type~I GRBs, which originate from compact binary mergers, and Type~II GRBs, which originate from massive collapsars. The traditi…
Estimating the peak energy of Swift gamma-ray bursts using supervised machine learning
Wan-Peng Sun, Si-Yuan Zhu, Da-Ling Ma +1
Gamma-ray bursts (GRBs) are among the most energetic explosive phenomena in the Universe, and their peak energy () is a key physical quantity for understanding the promp…
Unveiling the spectral morphological division of fast radio bursts with CHIME/FRB Catalog 2
Wan-Peng Sun, Yin-Long Cao, Yong-Kun Zhang +7
Fast radio bursts (FRBs) are commonly classified into repeating and apparently nonrepeating sources, yet whether this distinction reflects intrinsically different physical populati…
A Practical Framework for Estimating the Repetition Likelihood of Fast Radio Bursts from Spectral Morphology
Wan-Peng Sun, Yong-Kun Zhang, Ji-Guo Zhang +6
The repeating behavior of fast radio bursts (FRBs) is regarded as a key clue to understanding their physical origin, yet reliably distinguishing repeaters from apparent non-repeate…
Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters
Si-Yuan Zhu, Lang Shao, Pak-Hin Thomas Tam +1
Gamma-ray bursts (GRBs) are generally believed to originate from two distinct progenitors, compact binary mergers and massive collapsars. Traditional and some recent machine learni…
Comprehensive Statistical Analysis of Initial Lorentz Factor and Jet Opening Angle of Gamma-Ray Bursts
Jian Zhang, Bao-Cheng Qin, Lu-Lu Zhang +1
The initial Lorentz factor () and jet half-opening angle () of gamma-ray bursts (GRBs) are critical physical parameters for understanding the dynamical evolutio…
Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning
Si-Yuan Zhu, Hui-Ying Deng, Fu-Wen Zhang +2
Gamma-ray bursts (GRBs) are classified as Type I GRBs originated from compact binary mergers and Type II GRBs originated from massive collapsars. While Type I GRBs are typically sh…
Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning
Wan-Peng Sun, Ji-Guo Zhang, Yichao Li +4
Fast radio bursts (FRBs) are enigmatic high-energy events with unknown origins, which are observationally divided into two categories, i.e., repeaters and non-repeaters. However, t…
Classification of Fermi Gamma-Ray Bursts Based on Machine Learning
Si-Yuan Zhu, Wan-Peng Sun, Da-Ling Ma +1
Gamma-ray bursts (GRBs) are typically classified into long and short GRBs based on their durations. However, there is a significant overlapping in the duration distributions of the…