arXiv subjects

Xunhao Chen

Publications and source records attributed to Xunhao Chen.

4 recordsLinked to original sources

arXiv2026

SN 2024abfl: A Low-Luminosity Type IIP Supernova in NGC 2146 from a Low-Mass Red Supergiant Progenitor

Type IIP supernovae (SNe IIP) exhibit a significant diversity in their explosion properties, yet the physical mechanisms driving this diversity remain unknown. In this work, we present photometric and spectroscopic observations of SN 2024abfl, a SN IIP in NGC 2146 with a directly detected red supergiant (RSG) progenitor. We find it has a low plateau luminosity ($M_V \sim -15$ mag) and a relatively long plateau length ($\sim 126.5$ days). By fitting a semi-analytical model, we estimated a $^{56}$Ni mass of $\sim 0.009 M_\odot$, an initial kinetic energy of $\sim 0.42$ foe, an initial thermal energy of $\sim 0.03$ foe and an ejecta mass of $\sim 8.3 M_\odot$. The spectral evolution of SN 2024abfl is similar to those of other SNe IIP, except for much lower ejecta velocities at similar epochs. At later epochs, we find a relatively high-velocity H$α$ absorption feature at $\sim -4000$ km s$^{-1}$, possibly due to a fast-moving plume of matter in the inner ejecta, and two emission features at $\pm 2000$ km s$^{-1}$, possibly caused by CSM interaction. We estimate the progenitor mass to be $\le 15 M_\odot$ based on nebular spectra. We conclude that SN 2024abfl is a low-luminosity SN IIP originating from a low-mass RSG progenitor.

astro-ph.HE↗

arXiv2025

StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations

The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for surveys like the Nearby Galaxy Supernovae Survey. By integrating large language models with specialized function calls and modular workflows, StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection. The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers. Deployed across Nearby Galaxy Supernovae Survey's network of 10 amateur telescopes, the StarWhisper Telescope has detected transients with promising response times relative to existing surveys. Furthermore, StarWhisper Telescope's scalable agent architecture provides a blueprint for future facilities like the Global Open Transient Telescope Array, where AI-driven autonomy will be critical for managing 60 telescopes.

astro-ph.IM↗

arXiv2025

The Mini-SiTian Array: Evaluation Camera System

The Mini-SiTian project, which is the pathfinder for the SiTian project, utilizes three 30 cm telescopes equipped with commercial CMOS cameras (ZWO ASI6200MM Pro) to simulate large-area time-domain survey. Due to the avoidance of the traditional mechanical shutter, the CMOS camera is favorable in time-domain survey projects. In the future, the SiTian telescope array will employ a two-by-two scientific-grade mosaic CMOS camera to survey a 10,000-degree square area every 30 minutes. Therefore, the performance of CMOS directly determines the detection capability of SiTian telescopes for transient sources, and a comprehensive understanding of the performance of CMOS cameras is crucial. In this research, laboratory testing was conducted to thoroughly evaluate three cameras by assessing several critical parameters, including bias stability, dark current, pixel anomalies, linearity, gain, and read noise. We find exceptional short-term bias stability with standard deviations below 0.02 ADU, negligible dark current of approximately 0.002 e$^{-}$ pixel$^{-1}$ s$^{-1}$ at $0^\circ\text{C}$, and excellent linearity with nonlinearity consistently below $\pm$ 0.5\%, and a small proportion (0.06\% to 0.08\%) of pixels with anomalous responses. Furthermore, our analysis demonstrates uniform gain values across all cameras, ranging from 0.252 to 0.255 e$^{-}$ ADU$^{-1}$, with low readout noise, measured to be below 1.6 e$^{-}$ using conventional methods. We also propose a novel method for pixel-level gain and read noise calculation for CMOS sensors, which revealed a narrow gain distribution and a low median read noise of 1.028 e$^-$ for one of the cameras. The laboratory testing of the ZWO ASI6200MM Pro cameras indicates their potential to meet the requirements of time-domain surveys for the Mini-SiTian project.

astro-ph.IM↗

arXiv2023

MLP-AMDC: An MLP Architecture for Adaptive-Mask-based Dual-Camera snapshot hyperspectral imaging

Coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages over traditional methods in dynamically acquiring Hyper-Spectral Image (HSI), but there are the following problems. 1) Traditional mask relies on random patterns or analytical design, both of which limit the performance improvement of CASSI. 2) Existing high-quality reconstruction algorithms are slow in reconstruction and can only reconstruct scene information offline. To address the above two problems, this paper designs the AMDC-CASSI system, introducing RGB camera with CASSI based on Adaptive-Mask as multimodal input to improve the reconstruction quality. The existing SOTA reconstruction schemes are based on transformer, but the operation of self-attention pulls down the operation efficiency of the network. In order to improve the inference speed of the reconstruction network, this paper proposes An MLP Architecture for Adaptive-Mask-based Dual-Camera (MLP-AMDC) to replace the transformer structure of the network. Numerous experiments have shown that MLP performs no less well than transformer-based structures for HSI reconstruction, while MLP greatly improves the network inference speed and has less number of parameters and operations, our method has a 8 db improvement over SOTA and at least a 5-fold improvement in reconstruction speed. (https://github.com/caizeyu1992/MLP-AMDC.)

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