<tt>PhDLspec</tt> : Physical Prior Embedded Deep Learning Method for Spectroscopic Determination of Stellar Labels in High-dimensional Parameter Space

Abstract Unlocking all the physical information encoded in low-resolution spectra poses a significant challenge for astronomical survey analysis. Such a task demands modeling spectra and optimizing astrophysical parameters in high-dimensional space as a consequence of line blending. Here we present PhDLspec —a deep learning framework embedded with physical priors for stellar spectrum modeling and analysis. By imposing differential spectra derived from ab initio stellar atmospheric model calculation on a Transformer framework, PhDLspec can rigorously and precisely model stellar spectra by simultaneously taking into account more than 30 physical parameters at a computational speed hundreds of times faster than ab initio model calculation. With such a flexible stellar modeling approach, PhDLspec can effectively derive ∼30 stellar labels from a low-resolution spectrum using affordable optimization techniques. Application to LAMOST spectra ( R ≲ 1800) yields stellar elemental abundances in good agreement with high-resolution spectroscopic surveys, after essential calibrations to correct systematic biases in elemental abundance estimates using wide binaries and reference high-resolution data sets. We provide a catalog of 25 elemental abundances for 116,611 subgiant stars with precise age estimates. The successful application of PhDLspec to LAMOST spectra for high-dimensional parameter determination sheds light on similar challenges faced by other surveys and disciplines.