arXiv subjects

Wan-Peng Sun

Publications and source records attributed to Wan-Peng Sun.

7 recordsLinked to original sources

arXiv2026

Random Polarization Position Angle Behaviors across Bursts of Repeating Fast Radio Bursts

Fast radio bursts (FRBs), highly polarized, mostly have a nearly constant polarization position angle (PA) during each burst. Their PAs are observed to vary from burst to burst, with the statistical properties remaining stable across different observation sessions. We found that the intrinsic PAs of repeating FRBs are approximately Gaussian distributed, suggesting that the emission likely originates from a localized region within the neutron star's magnetosphere. A periodicity search of the PA time series using the Lomb-Scargle periodogram reveals no credible periodic signal in the period range from 10 ms to $10^7$ ms, and similar analyses of several active observations also yield null detections. We interpret these properties by extending the rotating vector model to include a dynamically evolving magnetosphere, in which the effective magnetic axis varies from burst to burst due to stochastic perturbations. In this framework, the observed PA distributions can naturally arise from geometric projection effects, and the absence of periodicity reflects the random wandering of the magnetic axis within a confined region. This scenario provides a natural explanation for both repeating and apparently non-repeating FRBs.

astro-ph.HE↗

arXiv2026

Estimating the peak energy of Swift gamma-ray bursts using supervised machine learning

Gamma-ray bursts (GRBs) are among the most energetic explosive phenomena in the Universe, and their peak energy ($E_{\rm p}$) is a key physical quantity for understanding the prompt emission mechanism. However, due to the limited energy coverage of the Swift satellite, a large fraction of Swift GRBs lack reliable peak energy measurements. Therefore, developing an accurate and efficient method for estimating $E_{\rm p}$ is of great importance. In this work, we propose a method based on the SuperLearner framework that integrates multiple supervised machine learning algorithms to estimate the $E_{\rm p}$ of Swift/BAT GRBs. We used the Swift/BAT observational data from December 2004 to September 2022 as training features, and adopted the peak energies of 516 GRBs jointly detected by Swift and either Fermi/GBM or Konus-Wind as training labels. After training and testing multiple supervised models, the final SuperLearner ensemble yields a more robust and reliable predictive model. In 100 iterations of five-fold cross-validation, the estimated $E'_{\rm p}$ values show a tight correlation with the observed $E_{\rm p}$, with an average Pearson correlation coefficient of $r = 0.72$. Compared with previous Bayesian estimates, our model provides estimations that are likely closer to the true values. Based on the trained model, we further estimated the peak energies of 650 Swift GRBs, significantly increasing the number of GRBs with estimated peak energies and providing new statistical support for constraining GRB emission mechanisms and energy origins.

astro-ph.HE↗

arXiv2026

Unveiling the spectral morphological division of fast radio bursts with CHIME/FRB Catalog 2

Fast radio bursts (FRBs) are commonly classified into repeating and apparently nonrepeating sources, yet whether this distinction reflects intrinsically different physical populations remains uncertain. Using the Second CHIME/FRB Catalog, we apply an unsupervised machine learning framework combining Uniform Manifold Approximation and Projection (UMAP) with density-based clustering to investigate the intrinsic structure of the FRB population in a multi-dimensional parameter space. We find that FRBs are primarily separated into two robust clusters dominated by spectral morphology. One cluster is characterized by narrowband emission and longer durations, while the other exhibits relatively broadband spectra and shorter burst timescales. This classification scheme achieves a recall of 0.94 for known repeaters. Within the repeating population, we further identify a stable subclass of atypical repeaters that are broadband, shorter in duration, and more luminous, resembling nonrepeating bursts. Furthermore, broadband nonrepeaters exhibit systematically higher dispersion measures (by approximately 200 $\text{pc cm}^{-3}$) and isotropic luminosities approximately an order of magnitude larger than those of repeating FRBs. Without invoking catastrophic progenitor scenarios, these differences are naturally explained by instrumental sensitivity limits and distance-dependent selection effects. Our results provide new statistical evidence for a physical connection between repeating and nonrepeating FRBs.

astro-ph.HE↗

arXiv2025

A Practical Framework for Estimating the Repetition Likelihood of Fast Radio Bursts from Spectral Morphology

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-repeaters with current observations remains challenging. Here we propose a physically interpretable and practically quantifiable classification framework based on spectral morphology. Using dimensionality reduction, clustering, and feature-importance analysis, we identify the spectral running $r$ and spectral index $\gamma$ as the most critical parameters for distinguishing repeaters from apparent non-repeaters in the CHIME/FRB sample. In the $\gamma$-$r$ space, repeaters preferentially occupy regions with steeper, narrower-band spectra, whereas non-repeaters cluster in flatter, broader-band regions, resulting in a clear density separation. We further construct an empirical probability map in the $\gamma$-$r$ space, showing a clear gradient of repetition likelihood, from $\sim 65\%$ in the high-repetition region to $\sim 5\%$ in the low-repetition region. Combining this with Gaussian Mixture Model posterior analysis, we identify several apparent non-repeaters with high inferred repetition probability, recommending them as priority targets for future monitoring. This framework provides a simple and generalizable tool for assessing repeatability in the CHIME/FRB sample and highlights the diagnostic power of spectral morphology in unveiling FRB origins.

astro-ph.HE↗

arXiv2025

Cosmic baryon census with fast radio bursts and gravitational waves

The cosmic baryon density fraction ($\Omega_{\rm b}$) is intrinsically correlated with the Hubble constant ($H_0$) through the critical density of the Universe. In the context of the decade-long $H_0$ tension, the significant discrepancy between early- and late-Universe measurements of $H_0$ implies that fixing its value or imposing an external prior could bias the baryon census. To address this concern, we construct a late-Universe probe framework that unifies fast radio bursts (FRBs) and gravitational-wave (GW) standard sirens, which can respectively resolve the ''missing baryon'' problem and the $H_0$ tension through their dispersion measures (DMs) and absolute luminosity distances. By combining $104$ localized FRBs with $47$ GW events, we obtain an $H_0$-free measurement of $\Omega_{\rm b}=0.0488\pm0.0064$ ($1\sigma$), in concordance with early-Universe observations of CMB + BBN. The result is tightly anchored by GW-inferred $H_0$ through the strong $\Omega_{\rm b}$-$H_0$ degeneracy. Although the current precision ($\sim 13\%$) is limited by sample size, the growing detections of both FRBs and GWs will make their synergy a powerful probe of low-redshift cosmology.

astro-ph.CO↗

arXiv2024

Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning

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, there are potentially a number of non-repeaters that may be misclassified, as repeating bursts are missed due to the limited sensitivity and observation periods, thus misleading the investigation of their physical properties. In this work, we propose a repeater identification method based on the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm and apply the classification to the first Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) catalog. We find that the spectral morphology parameters, specifically spectral running ($r$), represent the key features for identifying repeaters from the non-repeaters. Also, the results suggest that repeaters are more biased towards narrowband emission, whereas non-repeaters are inclined toward broadband emission. We provide a list of 163 repeater candidates, 5 of which are confirmed with an updated repeater catalog from CHIME/FRB. Our findings improve our understanding of the various properties underlying repeaters and non-repeaters, as well as guidelines for future FRB detection and categorization.

astro-ph.HE↗

arXiv2024

Classification of Fermi Gamma-Ray Bursts Based on Machine Learning

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 these two categories. In this paper, we apply the unsupervised dimensionality reduction algorithm called t-SNE and UMAP to classify 2061 Fermi GRBs based on four observed quantities: duration, peak energy, fluence, and peak flux. The map results of t-SNE and UMAP show a clear division of these GRBs into two clusters. We mark the two clusters as GRBs-I and GRBs-II, and find that all GRBs associated with supernovae are classified as GRBs-II. It includes the peculiar short GRB 200826A, which was confirmed to originate from the death of a massive star. Furthermore, except for two extreme events GRB 211211A and GRB 230307A, all GRBs associated with kilonovae fall into GRBs-I population. By comparing to the traditional classification of short and long GRBs, the distribution of durations for GRBs-I and GRBs-II do not have a fixed boundary. We find that more than 10% of GRBs-I have a duration greater than 2 seconds, while approximately 1% of GRBs-II have a duration shorter than 2 seconds.

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