利用机器学习估计低质量恒星的有效温度并在LAMOST DR10中识别金牛座T型星
Effective temperatures estimation of low-mass stars and identification of T Tauri stars in LAMOST DR10 using machine learning
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中文总结 AI 辅助
利用机器学习，通过自动测量关键光谱特征等效宽度，训练回归模型预测低质量恒星有效温度，应用逻辑回归模型识别金牛座T型星候选体，经蒙特卡罗框架估计相关不确定性，取得较好结果。
中文摘要 AI 辅助
我们展示了从自动光谱测量和应用于LAMOST DR10 V2巡天的低复杂度机器学习模型得出的低质量恒星的有效温度（Teff）估计值以及金牛座T型星（TTS）候选探测结果。对LAMOST观测的1千秒差距内的所有恒星自动测量关键诊断光谱特征的等效宽度。利用九个光谱特征作为输入，训练基于梯度提升机树的回归模型预测2500 - 5100K范围内的Teff，应用逻辑回归模型识别TTS候选体。两个模型在验证测试中表现出色。最后，采用蒙特卡罗框架传播输入不确定性，估计低质量恒星的Teff及其相关不确定性，并从光谱中识别候选TTS。
英文摘要
We present effective temperature (Teff) estimates of low-mass stars and T Tauri stars (TTS) candidate detections derived from automated spectroscopic measurements and low-complexity machine-learning models applied to the LAMOST DR10 V2 survey. Equivalent widths of key diagnostic spectral features, including the atomic lines Halpha, LiI 6708 AA and TiO/VO molecular bands, are automatically measured for all stars within 1 kpc observed by LAMOST. Using nine spectral features as inputs, we train a Gradient Boosting Machine tree-based regression model, calibrated with synthetic spectra from the PHOENIX library, to predict Teff over the range 2,500 - 5,100 K. We apply a logistic regression model to the principal components derived from the measured spectral features, enabling efficient identification of TTS candidates. Both models exhibit strong performance in validation tests. Finally, a Monte Carlo framework is employed to propagate input uncertainties and estimate Teff and its associated uncertainties for low-mass stars from 1,733,802 spectra and to identify 2,534 candidate TTS from 3,121 spectra.
