arXiv subjects

Si-Yuan Zhu

Publications and source records attributed to Si-Yuan Zhu.

9 recordsLinked to original sources

arXiv2026

Universal scaling between magnetar field and initial spin period for short gamma ray bursts

The $B_p$--$P_0$ correlation serves as a critical probe of magnetar engine physics. Although this scaling relation has been firmly established for long gamma-ray bursts (lGRBs), systematic investigations for short GRBs (sGRBs) remain absent, leaving the physical differences between the two populations poorly constrained. Here we analyze 33 Swift sGRBs exhibiting prominent X-ray plateaus from newborn millisecond magnetar spin-down, and derive their initial spin period $P_0$ and polar magnetic field $B_p$. sGRB magnetars span $P_0 \in [1.73,\,18.28]\ \mathrm{ms}$ and $B_p \in [0.06,\,2.82] \times 10^{17}\ \mathrm{G}$ ($\langle B_p \rangle = 7.05 \times 10^{16}\ \mathrm{G}$), significantly more magnetized than lGRB magnetars ($B_p \in [0.39,\,23.08] \times 10^{15}\ \mathrm{G}$; $\langle B_p \rangle = 3.69 \times 10^{15}\ \mathrm{G}$). For the first time, we derive consistent power-law $B_p$--$P_0$ correlations for GRBs : the scaling for sGRBs is $\log B_p = (0.84\pm0.07)\log P_0 + (15.79\pm0.07)$, whose slope is highly consistent with that of lGRBs, $\log B_p = (0.83\pm0.09)\log P_0 + (14.92\pm0.06)$. The near-identical slopes imply a universal magnetar spin-down mechanism, while the vertical offset between intercepts traces divergent progenitor channels. This scaling relation thus offers a new diagnostic to disentangle the formation pathways of GRB. Within the framework of the standard spin-up model, the mass accretion rates of sGRBs ($\dot{M} \sim 1 \times 10^{-1}$ to $3 \times 10^{-1}\,M_\odot\,\mathrm{s}^{-1}$) are substantially higher than those of lGRBs ($\dot{M} \sim 10^{-4}$ to $1 \times 10^{-1}\,M_\odot\,\mathrm{s}^{-1}$). Our work completes the missing $B_p$--$P_0$ statistics for sGRBs, quantitatively unifies their magnetar physics with lGRBs, and provides new observational constraints on the origin diversity of relativistic transients.

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arXiv2026

Identifying Merger-Driven and Collapsar-Driven Gamma-Ray Bursts with Precursor based Solely on Prompt Emission

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 seconds, 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-SNE and UMAP, 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 $E_{\rm p,ME}$-precursor index ($EPI$), defined as ${\rm log_{10}}(E_{\rm p,ME}^{2}/(T_{\rm 100,PE}T_{\rm 100,QE1}^{1/2}T_{\rm MVT,PE}))$, 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.

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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.

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arXiv2025

The Cosmic Star Formation History: Insights from Kilonova-Associated Gamma-Ray Bursts

The origin of the Universe and its material content remains one of the most fundamental questions in science. Gamma-ray bursts (GRBs), with their extreme luminosities and high-redshift detectability, provide a unique window into the history of cosmic formation and chemical evolution. Consequently, the GRB formation rate (FR) has been employed to trace the star formation rate (SFR) across cosmic time. GRBs are conventionally classified into long and short categories (lGRBs and sGRBs) based on their $ T_{90} $ duration. sGRBs are widely employed as tracers of the delayed SFR, owing to their origin linked to the inspiral timescales of compact binary systems. However, some studies suggest that the detection of supernova-associated sGRBs may indicate potential contamination by core-collapse events. In this work, we move beyond the $ T_{90} $ classification and focus exclusively on GRBs with confirmed kilonova signatures, which provide unambiguous evidence of binary compact star mergers, to reassess their connection with the delayed SFR. Through analysis of a kilonova-associated GRB (KN/GRBs) sample, we find that even within this robust subset, the KN/GRB FR displays a trend contrary to that of the delayed SFR at low redshifts ($ z < 1 $). This result challenges the conventional theory by indicating that low-redshift KN/GRBs may not accurately trace the delayed SFR, independent of core-collapse contamination, while further validation with larger KN/GRB samples is essential to determine the reliability of compact binary mergers as probes of delayed SFR.

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arXiv2025

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters

Gamma-ray bursts (GRBs) are generally believed to originate from two distinct progenitors, compact binary mergers and massive collapsars. Traditional and some recent machine learning-based classification schemes predominantly rely on observer-frame physical parameters, which are significantly affected by the redshift effects and may not accurately represent the intrinsic properties of GRBs. In particular, the progenitors usually could only be decided by successful detection of the multi-band long-term afterglow, which could easily cost days of devoted effort from multiple global observational utilities. In this work, we apply the unsupervised machine learning (ML) algorithms called t-SNE and UMAP to perform 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. Comparative analysis with conventional classification methods using $T_{90}$ and $E_{\rm p,z}$--$E_{\rm iso}$ correlation demonstrates that our machine learning approach provides superior discriminative power, particularly in resolving ambiguous cases of hybrid GRBs.

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arXiv2025

Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning

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 shorter than 2 seconds, recent observations suggest that some extend to tens of seconds, forming a potential subclass, Type IL GRBs. However, apart from their association with kilonovae, so far no rapid identification is possible. Given the uncertainties and limitations of optical and infrared afterglow observations, an identification method based solely on prompt emission can make such identification possible for many more GRBs. Interestingly, two established Type IL GRBs: GRB 211211A and GRB 230307A, exhibit a three-episode structure: precursor emission (PE), main emission (ME), and extended emission. Therefore, we comprehensively search for GRBs in the Fermi/GBM catalog and identify 29 three-episode GRBs. Based on 12 parameters, we utilize machine learning to distinguish Type IL GRBs from Type II GRBs. Apart from GRB 211211A and GRB 230307A, we are able to identify six more previously unknown Type IL GRBs: GRB 090831, GRB 170228A, GRB 180605A, GRB 200311A, GRB 200914A, and GRB 211019A. We find that Type IL GRBs are characterized by short duration and minimum variability timescale of PE, a short waiting time between PE and ME, and that ME follows the $E_{\rm p,z}$--$E_{\rm iso}$ correlation of Type I GRBs. For the first time, we identify a high-significant PE in the confirmed Type IL GRB 060614.

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arXiv2024

Unveiling the progenitors of a population of likely peculiar GRBs

Traditionally, gamma-ray bursts (GRBs) are classified as long and short GRBs, with $T_{90} = 2$ s being the threshold duration. Generally, long-duration GRBs (LGRBs, $T_{90}>2$ s) are associated with the collapse of massive stars, and short-duration (SGRBs, $T_{90}<2$ s) are associated with the compact binary mergers involving at least one neutron star. However, the existence of a population of so-called ``peculiar GRBs", i.e., LGRBs originating from mergers, or long Type I GRBs, and SGRBs originating from collapsars, or short Type II GRBs, have challenged the traditional paradigm of GRB classification. Finding more peculiar GRBs may help to give us more insight into this issue. In this work, we analyze the properties of machine learning identified long Type I GRBs and short Type II GRBs candidates, long GRBs-I and short GRBs-II (the so-called ``peculiar GRBs"). We find that long GRBs-I almost always exhibit properties similar to Type I, which suggests that the merger may indeed produce GRBs with $T_{90}>2$ s. Furthermore, according to the probability given by the redshift distribution, short GRBs-II almost exhibit properties similar to Type II. This suggests that the populations of short Type II GRBs are not scarce and that they are hidden in a large number of samples without redshifts, which is unfavorable to the interpretation that the jet progression leads to a missed main emission.

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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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arXiv2019

Characteristics of Long Gamma-Ray bursts in the Comoving Frame

We compile a sample of 93 long gamma-ray bursts (GRBs) from Fermi satellite and 131 from Konus-Wind, which have measured redshifts and well determined spectra, and estimate their pseudo Lorentz factors (${Γ_0}$) using the tight ${L_{\rm{iso}}}$-${E_{\rm p}}$-${Γ_0}$ correlation. The statistical properties and pair correlations of temporal and spectral parameters are studied in the observer frame, rest frame and comoving frame, respectively. We find that the distributions of the duration, peak energy, isotropic energy and luminosity in the different frames are basically lognormal, and their distributions in the comoving frame are narrow, clustering around $T'_{\rm 90}\sim 4000$ s, $E'_{\rm p,c}\sim 0.7$ keV, $E'_{\rm iso,c} \sim 8\times10^{49}$ erg and $L'_{\rm iso,c}\sim 2.5\times10^{46}$ erg s$^{-1}$, where the redshift evolution effect has been taken into account. We also find that the values of ${Γ_0}$ are broadly distributed between few tens and several hundreds with median values $\sim 270$. We further analyze the pair correlations of all the quantities, and well confirm ${E_{\rm{iso}}}$-${E_{\rm p}}$, ${L_{\rm{iso}}}$-${E_{\rm p}}$, ${L_{\rm{iso}}}$-${Γ_0}$ and $E_{\rm{iso}}$-${Γ_0}$ relations, and find that the corresponding relations in the comoving frame do still exist, but have large dispersions. This suggests not only the well-known spectrum-energy relations are intrinsic correlations, but also the observed correlations are governed by the Doppler effect. In addition, the peak energies of long GRBs are independent of durations both in the rest frame and in the comoving frame. And there is a weak anticorrelation between the peak energy and Lorentz factor.

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