Deep learning-driven atmospheric parameter prediction for hot subdwarf stars with synthetic and observed spectra

TL;DR

This paper develops a CNN model with attention mechanisms to accurately and efficiently predict atmospheric parameters of hot subdwarf stars from spectra, matching traditional methods' accuracy but with much higher speed.

Contribution

The study introduces a novel CNN architecture with channel and spatial attention for hot subdwarf spectral analysis, achieving high accuracy and enabling large-scale identification.

Findings

Achieved mean absolute errors of 730 K in Teff, 0.09 dex in log g, and 0.03 dex in helium abundance.

Confirmed 1512 hot subdwarfs from LAMOST DR12, including 291 new identifications.

Model's speed and efficiency surpass traditional spectral fitting methods.

Abstract

We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise ratio (SNR) levels, with each SNR level containing 11 396 synthetic spectra and 945 observed spectra. The trained deep learning models achieves mean absolute errors (AME) in predicting hot subdwarf atmospheric parameters of 730 K for effective temperature (Teff ), 0.09 dex for surface gravity (log g), and 0.03 dex for helium abundance (log(nHe/nH)), respectively, which reaches the accuracy of traditional spectral fitting methods. Utilizing the trained deep learning models and low-resolution spectra from LAMOST DR12, we confirm 1512 hot subdwarfs from the catalog of hot subdwarf candidates, of which 291 are newly identified. Our results demonstrate that the deep learning model not only achieves accuracy comparable to traditional methods in obtaining hot subdwarf atmospheric parameters, but also far exceeds them in speed and efficiency, making it particularly suitable for the analysis of large datasets of hot subdwarf spectra.