arXiv subjects

Yinuo Ma

Publications and source records attributed to Yinuo Ma.

3 recordsLinked to original sources

arXiv2026

Overview of Ground-based Wide-Angle Cameras array

As one of the key ground-based facilities of the Chinese-French SVOM mission, the main scientific objectives of the Ground-based Wide Angle Camera array (GWAC) are to detect prompt optical emission of gamma-ray bursts or other short duration astronomical transients on a second-scale temporal resolution. GWAC is located at Xinglong observatory, China, and consists of 10 mounts and 40 cameras, providing a joint field of view of about 3600 square degrees.The detection ability is 16 magnitude in 10 seconds of exposure time in the visual band under the condition of the new moon phase. Here, we give an overview of GWAC and introduce the science motivation of the project, as well as the performance of the hardware and the software. The observation strategies and the data processing are briefly presented. The early sciences in the last 5 years since the first light are summarized.

astro-ph.IM↗

arXiv2025

Comparative Statistical Analysis of Prompt and Afterglow X-Ray Flares in Gamma-Ray Bursts: Insights into Extended Central Engine Activity

Gamma-ray bursts (GRBs) are the most energetic phenomena in the Universe, characterized by prompt gamma-ray emission followed by multiwavelength afterglows. X-ray flares, observed during the afterglow phase, are generally believed to originate from the prolonged activity of the central engine, though direct evidence has been scarce. In this study, we present a comprehensive statistical analysis of X-ray flares from 315 GRBs observed by the Swift/X-ray Telescope over nearly two decades. We categorize flares into prompt flares (occurring during the prompt emission phase) and afterglow flares and compare their temporal and luminosity properties. Our analysis reveals that both types of flares exhibit similar morphological characteristics, with prompt flares being brighter and occurring earlier than afterglow flares. We find strong correlations between flare parameters, such as peak time, duration, and luminosity, which follow consistent patterns across both flare types. These findings suggest that X-ray flares, regardless of their timing, share a common origin in the central engine's activity. Our results imply that the central engine's activity duration extends beyond the prompt gamma-ray emission phase, highlighting the importance of considering X-ray flares when studying GRB progenitors and central engine properties. This work provides robust statistical evidence supporting the central engine origin of X-ray flares and underscores the need for future observations with missions like the Space-based multi-band astronomical Variable Objects Monitor and Einstein Probe to further elucidate the nature of GRB central engines.

astro-ph.HE↗

arXiv2025

PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings

Unsupervised pathology detection trains models on non-pathological data to flag deviations as pathologies, offering strong generalizability for identifying novel diseases and avoiding costly annotations. However, building reliable normality models requires vast healthy datasets, as hospitals' data is inherently biased toward symptomatic populations, while privacy regulations hinder the assembly of representative healthy cohorts. To address this limitation, we propose PathoSCOPE, a few-shot unsupervised pathology detection framework that requires only a small set of non-pathological samples (minimum 2 shots), significantly improving data efficiency. We introduce Global-Local Contrastive Loss (GLCL), comprised of a Local Contrastive Loss to reduce the variability of non-pathological embeddings and a Global Contrastive Loss to enhance the discrimination of pathological regions. We also propose a Pathology-informed Embedding Generation (PiEG) module that synthesizes pathological embeddings guided by the global loss, better exploiting the limited non-pathological samples. Evaluated on the BraTS2020 and ChestXray8 datasets, PathoSCOPE achieves state-of-the-art performance among unsupervised methods while maintaining computational efficiency (2.48 GFLOPs, 166 FPS).

cs.CV↗