Searching for Galactic H ii Regions from the LAMOST Database Based on the Multihead WDCNN Model

Abstract

Abstract 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.