A diagnostic tool for the identification of Supernova Remnants

TL;DR

This paper introduces new diagnostic tools using line ratios and machine learning to more accurately distinguish supernova remnants from HII regions, improving classification efficiency.

Contribution

It presents a novel combination of line ratios and SVM models for better separation of SNRs and HII regions, surpassing traditional flux ratio methods.

Findings

Achieves 98.95% completeness in SNR detection.

Introduces a new diagnostic line ratio combination.

Provides a quantitative comparison of selection criteria.

Abstract

We present new diagnostic tools for distinguishing supernova remnants (SNRs) from HII regions. Up to now, sources with flux ratio [S II]/H$\rm{\alpha}$ higher than 0.4 have been considered as SNRs. Here, we present the combinations of three or two line ratios as more effective tools for the separation of these two kinds of nebulae, depicting them as 3D surfaces or 2D lines. The diagnostics are based on photoionization and shock excitation models (MAPPINGS III) analysed with Support Vector Machine (SVM) models for classification. The line-ratio combination that gives the most efficient diagnostic is: [O I]/H$\rm{\alpha}$ - [O II]/H$\rm{\beta}$ - [O III]/H$\rm{\beta}$. This method gives $98.95\% $ completeness in the SNR selection and $1.20\%$ contamination. We also define the [O I]/H$\rm{\alpha}$ SNR selection criterion and we measure its efficiency in comparison to other selection criteria.