arXiv subjects

Ke-Rui Zhu

Publications and source records attributed to Ke-Rui Zhu.

4 recordsLinked to original sources

arXiv2026

The Evolution of Thermal and Non-thermal Emission Components in GRB 250920C

We present a temporal and time-resolved spectral analysis of GRB 250920C using \textit{Fermi}/GBM and \textit{Swift}/BAT data. The prompt emission consists of two distinct episodes, EI and EII, separated by a significant quiescent interval. We perform Bayesian spectral fitting with empirical, thermal, composite, and physical synchrotron models. In EI, the spectra show clear evidence for an additional thermal component. The BB+PL model is preferred in most bright time bins, and joint \textit{Fermi}/GBM+\textit{Swift}/BAT fits further support the presence of this component. The blackbody temperature generally decreases with time, the blackbody flux follows the pulse profile, and the thermal flux fraction remains high. Fireball-parameter estimates give a photospheric Lorentz factor of a few hundred and an initial radius of $r_{0}\sim10^{9}$--$10^{10}$ cm, with $1+\sigma_{0}$ of order unity and $\eta\gg1$, supporting a thermally dominated baryonic outflow. In contrast, EII is dominated by non-thermal emission. Its low-energy photon indices do not significantly exceed the synchrotron line of death, and the spectra are well described by fast-cooling synchrotron radiation in a decaying magnetic field. The magnetization constraint gives $\sigma_{\min}\sim1.1$--$4.3$. Since these values exceed unity, they suggest that EII may be Poynting-flux dominated. These results therefore suggest a possible transition in GRB 250920C from a fireball-dominated EI phase to a Poynting-flux-dominated EII phase.

astro-ph.HE↗

arXiv2026

Modeling Gamma-Ray Burst Spectra with Convolutional Neural Networks: Fast-Cooling Synchrotron Emission in a Decaying Magnetic Field

The radiation mechanism of gamma-ray burst (GRB) prompt emission remains uncertain. Although the fast-cooling synchrotron model in a decaying magnetic field can account for the characteristic nonthermal spectral shape, its computational cost has limited its use in systematic observational fitting and statistical model comparison. We develop a convolutional neural network (CNN)-based spectral emulator for this physical model and train it on a large synthetic data set generated over a physically motivated parameter space. The trained network reproduces the numerical spectra with high fidelity while reducing the cost of spectral evaluation to the millisecond level. We then incorporate the emulator into a Bayesian spectral-analysis framework and apply it to the time-resolved spectra of GRB 231020A observed by Fermi/GBM. In most time intervals, the decaying-field fast-cooling synchrotron model provides better fits and smaller Bayesian information criterion values than the standard fast-cooling synchrotron model. These results suggest that a radially decaying magnetic field provides a plausible and more physically motivated interpretation of the prompt-emission spectrum of this burst, while also indicating that the emulator offers a practical route for large-sample Bayesian inference and systematic comparisons of GRB prompt-emission models.

astro-ph.HE↗

arXiv2026

Fast-Cooling Synchrotron in Decaying Magnetic Fields: Implications for the GRB Spectral Distribution

The prompt-emission spectra of gamma-ray bursts (GRBs) are commonly described by the empirical Band function. The typical low-energy spectral index is $\sim -1$, which poses a challenge to standard synchrotron radiation models. We systematically investigate a fast-cooling synchrotron model with a decaying magnetic field and test, within an observation-consistent pipeline, whether it reproduces the Band-fit parameter distributions in the GBM catalog, in a statistical sense. We solve the electron continuity equation with synchrotron, adiabatic, and synchrotron self-Compton cooling to obtain the time-dependent electron distribution and synthetic spectra; we then forward-fold through the GBM response matrices and recover $(\alpha, \beta, E_p)$ with Band fits. We find that magnetic-field decay can harden the recovered $\alpha$ relative to the fast-cooling limit in part of parameter space, but the effect is not robust and is sensitive to the location of $E_p$ within the finite band and to spectral curvature; varying key physical scales reshapes the recovered $\alpha$ distribution, indicating that catalog $\alpha$ often represents an effective in-band slope rather than the asymptotic index. SSC cooling provides modest additional hardening and, in our setups, does not stabilize $\alpha$ near the observed peak. Using Monte Carlo samples designed to mimic the observations, the model yields $\alpha$ mostly between $-1.5$ and $-0.8$, but remains centered around $\alpha \approx -1.5$. Overall, while decaying-field fast-cooling synchrotron can partially alleviate overly soft spectra expected from standard fast-cooling synchrotron emission, it still falls short of reproducing the GBM $\alpha$ distribution at the population level, implying that additional physical processes are required.

astro-ph.HE↗

arXiv2024

Classification and physical characteristics analysis of Fermi-GBM Gamma-ray bursts based on Deep-learning

The classification of Gamma-Ray Bursts has long been an unresolved problem. Early long and short burst classification based on duration is not convincing due to the significant overlap in duration plot, which leads to different views on the classification results. We propose a new classification method based on Convolutional Neural Networks and adopt a sample including 3774 GRBs observed by Fermi-GBM to address the $T_\text{90}$ overlap problem. By using count maps that incorporate both temporal and spectral features as inputs, we successfully classify 593 overlapping events into two distinct categories, thereby refuting the existence of an intermediate GRB class. Additionally, we apply the optimal model to extract features from the count maps and visualized the extracted GRB features using the t-SNE algorithm, discovering two distinct clusters corresponding to S-type and L-type GRBs. To further investigate the physical properties of these two types of bursts, we conduct a time-integrated spectral analysis and discovered significant differences in their spectral characteristics. The analysis also show that most GRBs associated with kilonovae belong to the S-type, while those associated with supernovae are predominantly L-type, with few exceptions. Additionally, the duration characteristics of short bursts with extended emission suggest that they may manifest as either L-type or S-type GRBs. Compared to traditional classification methods (Amati and EHD methods), the new approach demonstrates significant advantages in classification accuracy and robustness without relying on redshift observations. The deep learning classification strategy proposed in this paper provides a more reliable tool for future GRB research.

astro-ph.HE↗