Spectuner: A Framework for Automated Line Identification of Interstellar Molecules

TL;DR

Spectuner is an automated spectral line identification framework that significantly reduces manual effort in astrochemistry, achieving high recall and precision on hot core data, and is openly accessible for further research.

Contribution

The paper introduces Spectuner, a novel automated spectral line identification framework that improves efficiency and accuracy in astrochemical spectral analysis.

Findings

Achieves 74-93% recall in line identification

Attains 78-92% average precision

Reduces manual intervention in spectral analysis

Abstract

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.