一种方法用于推导400万高分辨率4MOST恒星光谱的自洽非局部热力学平衡天体物理参数，耗时半天，使用可逆神经网络
A method to derive self-consistent NLTE astrophysical parameters for 4 million high-resolution 4MOST stellar spectra in half a day with invertible neural networks
AI总结：
本文提出一种基于深度学习的高效方法，利用可逆神经网络在半天内处理400万高分辨率4MOST光谱，推导自洽的天体物理参数和化学丰度。
摘要：
现代光谱学调查获取数百万颗恒星的光谱。然而，传统光谱学方法往往计算成本高，难以处理大规模数据集。我们介绍了一种基于模拟的深度学习方法，用于高效分析即将使用的高分辨率4MOST光谱仪的高分辨率恒星光谱。我们使用合成非局部热力学平衡（NLTE）光谱集，通过Turbospectrum生成，以模拟4MOST观测，并训练了一个条件可逆神经网络（cINN）以预测自洽的恒星表面参数和化学丰度。cINN是一种神经网络架构，能够为目标恒星属性估计完整的后验分布，提供内在的不确定性估计。我们评估了训练好的cINN模型在合成数据和观测恒星光谱上的预测性能。我们发现，我们的新cINN在NLTE合成光谱上训练后，能够以信号噪声比为250 per Angstrom时，恢复恒星参数的平均误差（σ）分别为：33 K for T_eff，0.16 dex for log(g)，0.12 dex for [Fe/H]，0.1 dex for [Ca/Fe]，0.11 for [Mg/Fe]，和0.51 dex for [Li/Fe]。从Gaia-ESO / 4MOST / PLATO基准恒星观测光谱的分析中，我们验证了我们的NLTE估计的恒星参数和丰度与使用独立代码TSFitPy获得的结果一致。我们得出结论，NLTE cINN是稳健的，并且理论上可以在不到一天的时间内使用GPU加速处理400万高分辨率4MOST光谱。
英文摘要：
Modern spectroscopic surveys obtain spectra for millions of stars. However, classical spectroscopic methods can often be computationally expensive, rendering them impractical for the analysis of large datasets. We introduce a novel simulation-based deep-learning approach for the efficient analysis of high-resolution stellar spectra to be obtained with the upcoming high-resolution 4MOST spectrograph. We used a suite of synthetic non-local thermodynamic equilibrium (NLTE) spectra generated with Turbospectrum to mimic 4MOST observations and trained a conditional invertible neural network (cINN) for the purpose of predicting self-consistently stellar surface parameters and chemical abundances. The cINN is a neural network architecture that estimates full posterior distributions for the target stellar properties, providing an intrinsic uncertainty estimate. We evaluated the predictive performance of the trained cINN model on both synthetic data and observed spectra of stars. We found that our new cINN trained on NLTE synthetic spectra is capable of recovering stellar parameters with average errors ($σ$) of $33$ K for $T_\mathrm{eff}$, $0.16$ dex for $\log(g)$, and $0.12$ dex for [Fe/H], $0.1$ dex for [Ca/Fe], $0.11$ for [Mg/Fe], and $0.51$ dex for [Li/Fe], respectively, at a signal to noise ratio of 250 per Angstrom. From the analysis of the observed spectra of Gaia-ESO / 4MOST / PLATO benchmark stars, we verified that our NLTE estimates for stellar parameters and abundances are consistent with results obtained with the independent code TSFitPy. We conclude that the NLTE cINN is robust and can, theoretically, evaluate 4 million high-resolution 4MOST spectra in less than a day, using GPU acceleration.
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