arXiv subjects

Xiaoxiong Zuo

Publications and source records attributed to Xiaoxiong Zuo.

4 recordsLinked to original sources

arXiv2026

Long GRB 250916A: an Off-axis Powerlaw Jet with Thermal Cocoon

Some gamma-ray bursts (GRBs) exhibit precursor emission episodes preceding the main emission, with a quiescent period in between. The properties of the precursor emission and the duration of the quiescent interval are related to the central engine activity and jet formation processes, thus providing insights into the physics of GRBs. We present a comprehensive analysis of the prompt emission and multi-wavelength afterglow of GRB 250916A. Using detailed afterglow modeling, we find that the broadband data are best described by a powerlaw structured jet with a relatively narrow core ($θ_c \approx 0.8^\circ$), viewed moderately off-axis at a viewing angle $θ_v \approx 2.7^\circ$. The isotropic-equivalent kinetic energy of the jet ($E_{k,iso} \approx 2.4 \times 10^{54}$ erg) is on the higher side for typical GRBs. The precursor emission is well described by a blackbody spectrum with a temperature of kT $\approx$ 13.2 keV and is separated from the main emission by a long quiescent interval of 150 s. Put together, our results indicate that the precursor is likely to be a shock breakout from a cocoon formed by the interaction of the relativistic jet with the progenitor star. The resulting cocoon pressure and shock collimation naturally lead to the launch of a narrowly collimated jet, consistent with the jet geometry inferred from afterglow observations. The long quiescent interval may imply the central engine turn-off in addition to the effect of the off-axis geometry.

astro-ph.HE↗

arXiv2025

AT2025ulz and S250818k: Investigating early time observations of a subsolar mass gravitational-wave binary neutron star merger candidate

Over the past LIGO--Virgo--KAGRA (LVK) observing runs, it has become increasingly clear that identifying the next electromagnetic counterparts to gravitational-wave (GW) neutron star mergers will likely be more challenging compared to the case of GW170817. The rarity of these GW events, and their electromagnetic counterparts, motivates rapid searches of any candidate binary neutron star (BNS) merger detected by the LVK. We present our extensive photometric and spectroscopic campaign of the candidate counterpart AT2025ulz to the low-significance GW event S250818k, which had a ${\sim} 29\%$ probability of being a BNS merger. We demonstrate that during the first five days, the luminosity and color evolution of AT2025ulz are consistent with both kilonova and shock cooling models, although a Bayesian model comparison shows preference for the shock cooling model, underscoring the ambiguity inherent to early data obtained over only a few days. Continued monitoring beyond this window reveals a rise and color evolution incompatible with kilonova models and instead consistent with a supernova. This event emphasizes the difficulty in identifying the electromagnetic counterparts to BNS mergers and the significant allotment of observing time necessary to robustly differentiate kilonovae from impostors.

astro-ph.HE↗

arXiv2025

FALCO: a Foundation model of Astronomical Light Curves for time dOmain astronomy

Time-domain surveys have advanced astronomical research by revealing diverse variable phenomena, from stellar flares to transient events. The scale and complexity of survey data, along with the demand for rapid classification, present significant challenges for analysis. While machine learning offers solutions, most existing models are tailored to single tasks, struggle to generalize, and depend heavily on large, accurately labeled datasets. We introduce FALCO, a foundation model for astronomical light curve analysis in time-domain astronomy. This work presents the initial version of FALCO trained via self-supervised learning on unlabeled Kepler light curves using a Transformer-based architecture. The model has been evaluated on three distinct tasks and demonstrates strong generalization: achieving 95 percent accuracy in stellar variability classification across eight classes, an overall RMSE of 0.1305 dex in surface gravity estimation (notably improved to below 0.08 dex when log g is less than 1, and approximately 0.02 dex near log g equals 3), and 87 percent precision in flare identification. These results highlight the model's versatility and ability to learn generalizable representations from light curves, enabling straightforward adaptation to diverse tasks. We further analyze the impact of model scaling and sequence length, finding performance improves with larger models and longer input sequences. We also apply FALCO to derive surface gravity (log g) measurements for 179,732 Kepler stars from their light curves.

astro-ph.IM↗

arXiv2024

X-ray Sources Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

astro-ph.IM↗