Modern surveys often deliver hundreds of thousands of stellar spectra at once, which are fit to spectral models to derive stellar parameters/labels. Therefore, the technique of Amortized Neural Posterior Estimation (ANPE) stands out as a suitable approach, which enables the inference of large number of targets as sub-linear/constant computational costs. Leveraging our new nbi software package, we train an ANPE model for the APOGEE survey and demonstrate its efficacy on both mock and real APOGEE stellar spectra. Unique to the nbi package is its out-of-the-box functionality on astronomical inverse problems with sequential data. As such, we have been able to acquire the trained model with minimal effort. We introduce an effective approach to handling the measurement noise properties inherent in spectral data, which utilizes the actual uncertainties in the observed data. This allows training data to resemble observed data, an aspect that is crucial for ANPE applications. Given the association of spectral data properties with the observing instrument, we discuss the utility of an ANPE "model zoo," where models are trained for specific instruments and distributed under the nbi framework to facilitate real-time stellar parameter inference.

借助Amortized Neural Posterior Estimation（ANPE）技术以及新的nbi软件包，本研究为APOGEE星光谱调查提供了一个训练有素的ANPE模型，展示了其在模拟和真实APOGEE星光谱上的功效。我们引入了一种有效处理光谱数据中固有测量噪声特性的方法，利用了观测数据中的实际不确定性，使训练数据能够类似于观测数据，这对于ANPE应用至关重要。此外，我们探讨了ANPE“模型库”的实用性，其中针对特定仪器进行模型训练，并在nbi框架下分发，以促进实时的恒星参数推断。

用摊销的神经后验估计和能量果蝇算法拟合恒星光谱