Improved Selection Criteria for HII Regions, based on IRAS Sources

TL;DR

This paper develops new IRAS-based criteria using machine learning to identify HII regions in the Milky Way, significantly increasing candidate numbers and providing insights into their evolution.

Contribution

It introduces a novel IRAS selection criterion for HII regions using SVM, enhancing candidate identification and estimating the total HII region population in the Milky Way.

Findings

Identified 3041 IRAS HII region candidates.

Proposed an optimal IRAS flux ratio criterion for HII region selection.

Estimated at least 10,200 HII regions in the Milky Way.

Abstract

We present new criteria for selecting HII regions from the Infrared Astronomical Satellite (IRAS) Point Source catalogue (PSC), based on an HII region catalogue derived manually from the all-sky Wide-field Infrared Survey Explorer (WISE). The criteria are used to augment the number of HII region candidates in the Milky Way. The criteria are defined by the linear decision boundary of two samples: IRAS point sources associated with known HII regions, which serve as the HII region sample, and IRAS point sources at high Galactic latitudes, which serve as the non-HII region sample. A machine learning classifier, specifically a support vector machine (SVM), is used to determine the decision boundary. We investigate all combinations of four IRAS bands and suggest that the optimal criterion is log(F$_{\rm 60}$/F$_{\rm 12}$)$\ge$(-0.19$\times$log(F$_{\rm 100}$/F$_{\rm 25}$)+ 1.52), with detections at 60 and 100 micron. This selects 3041 HII region candidates from the IRAS PSC. We find that IRAS HII region candidates show evidence of evolution on the two-colour diagram. Merging the WISE HII catalogue with IRAS HII region candidates, we estimate a lower limit of approximately 10200 for the number of HII regions in the Milky Way.