Using machine learning to classify the diffuse interstellar bands

TL;DR

This study employs machine learning on extensive extragalactic spectra to analyze diffuse interstellar bands, revealing their correlations with dust extinction and grouping them into spectroscopic families based on environmental dependencies.

Contribution

The paper introduces a machine learning approach to classify DIBs into families and analyze their environmental correlations, expanding understanding of DIB carriers and their relation to dust.

Findings

10 DIBs strongly correlate with dust extinction

A pair of DIBs shows negative correlation with dust

DIBs can be grouped into families based on environmental factors

Abstract

Using over a million and a half extragalactic spectra we study the correlations of the Diffuse Interstellar Bands (DIBs) in the Milky Way. We measure the correlation between DIB strength and dust extinction for 142 DIBs using 24 stacked spectra in the reddening range E(B-V) < 0.2, many more lines than ever studied before. Most of the DIBs do not correlate with dust extinction. However, we find 10 weak and barely studied DIBs with correlations that are higher than 0.7 with dust extinction and confirm the high correlation of additional 5 strong DIBs. Furthermore, we find a pair of DIBs, 5925.9A and 5927.5A which exhibits significant negative correlation with dust extinction, indicating that their carrier may be depleted on dust. We use Machine Learning algorithms to divide the DIBs to spectroscopic families based on 250 stacked spectra. By removing the dust dependency we study how DIBs follow their local environment. We thus obtain 6 groups of weak DIBs, 4 of which are tightly associated with C2 or CN absorption lines.