Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Probabilistic Inference ($\rm{deep-REMAP}$), a novel framework that utilizes the rich synthetic spectra from the PHOENIX library and observational data from the MARVELS survey to accurately predict stellar atmospheric parameters. By harnessing advanced machine learning techniques, including multi-task learning and an innovative asymmetric loss function, $\rm{deep-REMAP}$ demonstrates superior predictive capabilities in determining effective temperature, surface gravity, and metallicity from observed spectra. Our results reveal the framework's effectiveness in extending to other stellar libraries and properties, paving the way for more sophisticated and automated techniques in stellar characterization.

翻译：传统光谱分析方法正日益受到当代天文巡天项目爆发式增长数据的挑战。为此，我们提出基于正则化集成多任务学习与非对称损失函数的概率推断框架（deep-REMAP），该框架利用PHOENIX库的丰富合成光谱数据与MARVELS巡天观测数据，实现恒星大气参数的精确预测。通过融合多任务学习与创新性非对称损失函数等先进机器学习技术，deep-REMAP在基于观测光谱确定有效温度、表面重力及金属丰度方面展现出卓越的预测能力。研究结果表明，该框架可有效拓展至其他恒星库与参数预测，为恒星表征领域更复杂、更自动化的技术发展开辟了新路径。