Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi
This enables faster stellar parameter inference for astronomers dealing with hundreds of thousands of spectra, though it is incremental as it adapts existing ANPE methods to astronomical data.
The authors tackled the problem of efficiently fitting stellar spectra from large surveys by applying Amortized Neural Posterior Estimation (ANPE) with their nbi software package to the APOGEE survey, demonstrating its efficacy on both mock and real data with sub-linear/constant computational costs.
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.