用机器学习从恒星光谱中快速精准预测物理参数并生成仿真光谱。
Stellar parameter prediction and spectral simulation using machine learning
- 融合自编码器的监督与无监督学习，结合物理模拟生成真实感光谱。
- 有效温度预测误差约50K，金属丰度和表面重力精度达0.03dex和0.04dex。
- 模型在CPU上仅需779.6毫秒，适合大规模光谱巡天等高通量应用。
我们对欧洲南方天文台高精度径向速度行星搜索仪（HARPS）的历史数据进行了机器学习分析，主要目标是恢复观测天体的物理属性，次要目标是模拟光谱。系统研究了模拟数据、真实训练数据量、网络结构和学习范式等因素对结果准确性和保真度的影响。方法基于现有物理模拟模型，利用恒星光谱库通过第一性原理计算出射通量，并结合HARPS仪器模型生成与观测数据可比的仿真光谱。我们在真实HARPS数据上训练标准与变分自编码器，用于预测光谱参数并生成光谱。模型在预测光谱参数和压缩真实光谱方面表现优异，有效温度预测平均误差约为50 K，适用于多数天体物理应用；金属丰度([M/H])和表面重力(log g)预测精度分别达到约0.03 dex和0.04 dex。模型计算效率高，CPU处理时间为779.6毫秒，GPU仅需3.97毫秒，适用于大规模光谱巡天和档案研究。相比传统方法，本方法在保持相近精度的同时显著降低计算耗时，提升了光谱分析的范围与效率。
原文摘要 · Abstract (English)
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the results, including the use of simulated data, the effect of varying amounts of real training data, network architectures, and learning paradigms. Our approach integrates supervised and unsupervised learning techniques within autoencoder frameworks. Our methodology leverages an existing simulation model that utilizes a library of existing stellar spectra in which the emerging flux is computed from first principles rooted in physics and a HARPS instrument model to generate simulated spectra comparable to observational data. We trained standard and variational autoencoders on HARPS data to predict spectral parameters and generate spectra. Our models excel at predicting spectral parameters and compressing real spectra, and they achieved a mean prediction error of approximately 50 K for effective temperatures, making them relevant for most astrophysical applications. Furthermore, the models predict metallicity ([M/H]) and surface gravity (log g) with an accuracy of approximately 0.03 dex and 0.04 dex, respectively, underscoring their broad applicability in astrophysical research. The models' computational efficiency, with processing times of 779.6 ms on CPU and 3.97 ms on GPU, makes them valuable for high-throughput applications like massive spectroscopic surveys and large archival studies. By achieving accuracy comparable to classical methods with significantly reduced computation time, our methodology enhances the scope and efficiency of spectroscopic analysis.
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