Fu-Wen Zhang

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 traditional correspondence between short Type I GRBs and long Type II GRBs, separated by a duration of 2 s, has been challenged by recent observations of long GRBs associated with kilonovae (i.e., Type I-L GRBs) and a short GRB associated with a supernova. In this paper, we focus on GRBs with precursor emission (PE) and compile 366 GRBs detected by Fermi/GBM. Applying the unsupervised machine learning methods t -distributed stochastic neighbor embedding and uniform manifold approximation and projection, we are able to distinguish Type I (including subclass Type I-L) and Type II GRBs for the first time and identify PE as a key feature for distinguishing GRBs of different origins. Inspired by results of machine learning, we propose a diagnostic parameter, the E _p,ME -precursor index (EPI), defined as log 10 ( E p , ME 2 / ( T 100 , PE T 100 , QE1 1 / 2 T MV T , PE ) ) , where most Type I GRBs have EPI > 6.2 and most Type II GRBs have EPI < 6.2. This parameter can help the community to diagnose the origin of any GRB with PE based solely on its prompt emission and rapidly plan for follow-up observations. The validation using Swift GRBs provides illustrative evidence that our method may also be applicable to GRBs observed by instruments other than Fermi.

Gamma-ray bursts (GRBs) are among the most energetic explosive phenomena in the Universe, and their peak energy ( E _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 _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 _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 ′ _p values show a tight correlation with the observed E _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.

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 nonrepeaters 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 γ as the most critical parameters for distinguishing repeaters from apparent nonrepeaters in the CHIME/FRB sample. In the γ– r space, repeaters preferentially occupy regions with steeper, narrower-band spectra, whereas nonrepeaters cluster in flatter, broader-band regions, resulting in a clear density separation. We further construct a probability map in the γ – r space based on Gaussian mixture model posterior analysis, revealing a clear gradient of repetition likelihood from ∼67% in the high-repetition region to ∼7% in the low-repetition region. This model also identifies several apparent nonrepeaters with high inferred repetition probability, highlighting 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.

Gamma-ray bursts (GRBs) are generally believed to originate from two distinct progenitors, namely, compact binary mergers and massive collapsars. Traditional methods, along with a number of recent machine learning-based classification schemes, predominantly rely on observer-frame physical parameters, which are significantly affected by the redshift effects and might not accurately represent the intrinsic properties of GRBs. In particular, the progenitors could typically only be determined on the basis of a successful detection of the multiband long-term afterglow, which could easily cost days of devoted effort from multiple global observational utilities. In this work, we applied the unsupervised machine learning (ML) algorithms t-SNE and UMAP to perform the GRB classification based on rest-frame prompt emission parameters. The map results of both t-SNE and UMAP reveal a clear division of these GRBs into two clusters, denoted as GRBs-I and GRBs-II. We find that all supernova-associated GRBs, including the atypical short-duration burst GRB 200826A (now recognized as collapsar-origin), consistently fall within the GRBs-II category. Conversely, all kilonova-associated GRBs (except for two controversial events) are classified as GRBs-I, including the peculiar long-duration burst GRB 060614 originating from a merger event. In another words, this clear ML separation of two types of GRBs based only on prompt properties could correctly predict the results of progenitors without follow-up afterglow properties. A comparative analysis with conventional classification methods using T₉₀ and E_p, z–E_iso correlation demonstrates that our machine learning approach provides superior discriminative power, particularly with respect to resolving ambiguous cases of hybrid GRBs.

The initial Lorentz factor (Γ₀) and jet half-opening angle (θ _jet) of gamma-ray bursts (GRBs) are critical physical parameters for understanding the dynamical evolution of relativistic jets and the true energy release of GRBs. We compile a sample of 89 GRBs that exhibit an onset bump feature in their early optical or GeV light curves, 42 of which also display a jet break feature, and derive their Γ₀ and θ _jet values. Using this sample, we reexamine the correlations between Γ₀ and the prompt emission parameters (isotropic energy E _iso, peak luminosity L _iso, and peak energy E _p). Our results confirm the previously reported Γ₀−E _iso (L _iso), Γ₀−E _p,z, and E _iso (L _iso)−E _p−Γ₀ relations for both homogeneous interstellar medium (ISM) and Wind density profiles (Wind). Notably, we find that the short GRB 090510 complies with the Γ₀−E _iso relation, but significantly deviates from the Γ₀−L _iso and Γ₀−E _p,z relations. We systematically investigate the influence of θ _jet on Γ₀ and find a weak dependence, which reads Γ₀ (ISM) ∝ θ jet − 0.18 ± 0.15 (ISM) and Γ₀ (Wind) ∝ θ jet − 0.72 ± 0.14 (Wind). Additionally, we report, for the first time, three new three-parameter correlations, i.e., E iso ∝ θ jet − 0.52 ± 0.26 (ISM) Γ 0 2.21 ± 0.28 (ISM) and E iso ∝ θ jet − 0.65 ± 0.21 (Wind) Γ 0 2.08 ± 0.19 (Wind); L iso ∝ θ jet − 0.35 ± 0.27 (ISM) Γ 0 2.46 ± 0.29 (ISM) and L iso ∝ θ jet − 0.29 ± 0.28 (Wind) Γ 0 2.37 ± 0.25 (Wind); and E p , z ∝ θ jet − 0.15 ± 0.19 (ISM) Γ 0 1.11 ± 0.21 (ISM) and E p , z ∝ θ jet − 0.17 ± 0.23 (Wind) Γ 0 1.09 ± 0.21 (Wind). These tight E _iso (or L _iso, E _p,z)−Γ₀−θ _jet correlations likely arise from the combined effects of radiation mechanisms, jet structure, and outflow dynamics in GRBs. In addition, we further explore the relations between the initial Lorentz factor and the jet-corrected energy, and find Γ₀ (ISM) ∝ E γ , 52 0.22 ± 0.04 (ISM) and Γ₀ (Wind) ∝ E γ , 52 0.40 ± 0.06 (Wind); and Γ₀ (ISM) ∝ L γ , 52 0.20 ± 0.04 (ISM) and Γ₀ (Wind) ∝ L γ , 52 0.30 ± 0.06 (Wind). We also find that the jet-corrected correlations remain significant, suggesting that these relations are intrinsic to the physical nature of GRBs. However, the increased dispersion after correction implies that underlying differences persist among individual GRBs.

Fast radio bursts (FRBs) are enigmatic high-energy events with unknown origins, which are observationally divided into two categories, i.e., repeaters and nonrepeaters. However, there are potentially a number of nonrepeaters 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 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 nonrepeaters. Also, the results suggest that repeaters are more biased toward narrowband emission, whereas nonrepeaters are inclined toward broadband emission. We provide a list of 163 repeater candidates, five of which are confirmed with an updated repeater catalog from CHIME/FRB. Our findings improve our understanding of the various properties underlying repeaters and nonrepeaters, as well as guidelines for future FRB detection and categorization.