Interstellar molecules, which play an important role in astrochemistry, are identified using observed spectral lines. Despite the advent of spectral analysis tools in the past decade, the identification of spectral lines remains a tedious task that requires extensive manual intervention, preventing us from fully exploiting the vast amounts of data generated by large facilities such as ALMA. This study aims to address the aforementioned issue by developing a framework of automated line identification. We introduce a robust spectral fitting technique applicable for spectral line identification with minimal human supervision. Our method is assessed using published data from five line surveys of hot cores, including W51, Orion-KL, Sgr B2(M), and Sgr B2(N). By comparing the identified lines, our algorithm achieves an overall recall of ~ 74% - 93%, and an average precision of ~ 78% - 92%. Our code, named Spectuner, is publicly available on GitHub.
Spectuner-D1: Spectral Line Fitting of Interstellar Molecules Using Deep Reinforcement Learning
Zhejiang Laboratory（浙江实验室）；Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；Xinjiang Astronomical Observatory, Chinese Academy of Sciences（中国科学院新疆天文台）；University of Chinese Academy of Sciences（中国科学院大学）；I. Physikalisches Institut, Universität zu Köln（科隆大学第一物理研究所）
Automated Assignment and Prediction of Molecules in Astronomical Line Surveys Using Machine-Learning-Based Chemical Embeddings
Massachusetts Institute of Technology（麻省理工学院）；National Radio Astronomy Observatory（国家射电天文台）；Centre for Star and Planet Formation, Niels Bohr Institute & Natural History Museum of Denmark, University of Copenhagen（哥本哈根大学尼尔斯·玻尔研究所与丹麦自然历史博物馆恒星与行星形成中心）；Center for Astrophysics | Harvard & Smithsonian（哈佛-史密森天体物理中心）；University of British Columbia（不列颠哥伦比亚大学）
The Gaia-ESO Survey: Extracting diffuse interstellar bands from cool star spectra, DIB-based interstellar medium line-of-sight structures at the kpc scale
A Census of Large-Scale ($\ge$ 10 pc), Velocity-Coherent, Dense Filaments in the Northern Galactic Plane: Automated Identification Using Minimum Spanning Tree
VAPOLA -- A multi-year, multi-band polarization survey of AGN and Sgr A* at mm wavelengths with ALMA I. Survey Overview and Science-Ready Archival Products
Università degli Studi di Cagliari（卡利亚里大学）；INAF - Osservatorio Astronomico di Cagliari（意大利国家天体物理研究所卡利亚里天文台）；University of Balearic Islands（巴利阿里群岛大学）；Institute of the Balearic Islands (IAIB)（巴利阿里群岛研究所）；Health Research Institute of the Balearic Islands (IdISBa)（巴利阿里群岛健康研究所）；Universidade São Paulo, Instituto de Astronomia, Geofísica e Ciéencias Atmosféricas（圣保罗大学天文、地球物理与大气科学研究所）；INAF-Istituto di Radioastronomia（意大利国家天体物理研究所射电天文学研究所）；INAF–Italian Centre for Astronomical Archive（意大利天体物理研究所意大利天文档案中心）
