AI 中文总结
webSME是SME的在线扩展工具，集成多项增强功能，可高效便捷分析高分辨率恒星光谱，经合成与观测光谱验证，是现代天体物理的实用工具。
AI 中文摘要
恒星光谱学是确定有效温度、表面重力、金属丰度等基本恒星参数的可靠技术。Spectroscopy Made Easy（SME）长期以来一直是光谱合成与参数推断的框架。本文介绍webSME，它是SME Python实现的基于网络的扩展。webSME集成了多项增强功能，包括非局部热力学平衡丰度校正模式、用于从数千埃宽的大波长范围可靠确定恒星参数的预计算合成光谱网格、用于不确定性估计的马尔可夫链蒙特卡洛采样，以及对最新参考丰度模式的支持。它能够高效且便捷地分析高分辨率光谱，适用于从详细丰度研究到教育和科普推广的广泛应用。我们在合成光谱和观测光谱（包括基准恒星）上验证了webSME的性能，并将其准确性与基于经典SME的分析进行了对比。该平台的易访问性和先进功能使其成为现代天体物理工具集中的强大工具。
英文摘要
Stellar spectroscopy is a robust technique for determining fundamental stellar parameters such as effective temperature, surface gravity and metallicity. Spectroscopy Made Easy (SME) has long served as a framework for spectral synthesis and parameter inference. In this paper, we introduce webSME, a web-based extension of the Python implementation of SME. webSME integrates enhancements including a non-local thermodynamic equilibrium abundance correction mode, a precomputed grid of synthetic spectra for robust determination of stellar parameters from large wavelength ranges (thousands of Angstroms wide), Markov Chain Monte Carlo sampling for uncertainty estimation, and support for recent reference abundance patterns. It enables efficient and user-friendly analysis of high-resolution spectra, making it suitable for a wide range of applications - from detailed abundance studies to education and outreach. We demonstrate the performance of webSME on synthetic and observed spectra, including benchmark stars, and validate its accuracy against classical SME-based analysis. The platform's ease of access and advanced capabilities position it as a powerful tool in the modern astrophysical toolkit.
CommentsAccepted for publication in A&A (July, 2026) (14 pages)
