A diagnostic tool for the identification of supernova remnants

Abstract

ABSTRACT We present new diagnostic tools for distinguishing supernova remnants (SNRs) from H ii 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 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 $98.95{{\ \rm per\ cent}}$ completeness in the SNR selection and $1.20{{\ \rm per\ cent}}$ contamination. We also define the [O i]/H$\rm {\alpha }$ SNR selection criterion and measure its efficiency in comparison with other selection criteria.