# Searching for Hot Subdwarf Stars in LAMOST DR1-II. Pure spectroscopic identification method for hot subdwarfs

## TL;DR

This paper introduces a new machine learning method called HELM for spectroscopic identification of hot subdwarf stars, successfully discovering 56 such stars in LAMOST DR1 data and confirming known spectral features.

## Contribution

The paper presents the HELM algorithm, a novel machine learning approach that directly analyzes spectra for hot subdwarf identification without needing photometric data.

## Key findings

- Identified 56 hot subdwarf stars in LAMOST DR1.
- Confirmed the two He sequences in the Teff - log(nHe/nH) diagram.
- Demonstrated HELM's reliability and applicability to other spectral objects.

## Abstract

Employing a new machine learning method, named hierarchical extreme learning machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data release (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. The atmospheric parameters of the stars are obtained by fitting the profiles of hydrogen (H) Balmer lines and helium (He) lines with synthetic spectra calculated from non-Local Thermodynamic Equilibrium (NLTE) model atmospheres. Five He-rich hot subdwarf stars were found in our sample with their log(nHe/nH) > -1 , while 51 stars are He-poor sdB, sdO and sdOB stars. We also confirmed the two He sequences of hot subdwarf stars found by Edelmann et al. (2003) in Teff - log(nHe/nH) diagram. The HELM algorithm works directly on the observed spectroscopy and is able to filter out spectral properties without supplementary photometric data. The results presented in this study demonstrate that the HELM algorithm is a reliable method to search for hot subdwarf stars after a suitable training is performed, and it is also suitable to search for other objects which have obvious features in their spectra or images.