2023ApJS..267...39W

A H II region is a kind of emission nebula, and more definite samples of H II regions can help study the formation and evolution of galaxies. Hence, a systematic search for H II regions is necessary. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) conducts medium-resolution spectroscopic surveys and provides abundant valuable spectra for unique and rare celestial body research. Therefore, the medium-resolution spectra of LAMOST are an ideal data source for searching for Galactic H II regions. This study uses the LAMOST spectra to expand the current spectral sample of Galactic H II regions through machine learning. Inspired by deep convolutional neural networks with wide first-layer kernels (WDCNN), a new spectral-screening method, multihead WDCNN, is proposed and implemented. Infrared criteria are further used for the identification of Galactic H II region candidates. Experimental results show that the multihead WDCNN model is superior to other machine-learning methods and it can effectively extract spectral features and identify H II regions from the massive spectral database. In the end, among all candidates, 57 H II regions are identified and known in SIMBAD, and four objects are identified as “to be confirmed” Galactic H II region candidates. The known H II regions and H II region candidates can be retrieved from the LAMOST website.