Discovery of $\sim$2200 new supernova remnants in 19 nearby star-forming galaxies with MUSE spectroscopy

TL;DR

This study uses MUSE spectroscopy to identify over 2,200 supernova remnants in 19 nearby star-forming galaxies, employing multiple diagnostics to improve detection accuracy and validate findings against known catalogs.

Contribution

The paper presents the largest extragalactic SNR survey using integral field spectroscopy and introduces a novel line ratio technique to distinguish SNRs from other nebulae.

Findings

Identified 2,233 SNR candidates across 19 galaxies.

Validated detection method with M83, recovering 51% of known SNRs.

Found 61% of SNRs selected by [OI]/Hα line ratio.

Abstract

We present the largest extragalactic survey of supernova remnant (SNR) candidates in nearby star-forming galaxies using exquisite spectroscopic maps from MUSE. Supernova remnants exhibit distinctive emission-line ratios and kinematic signatures, which are apparent in optical spectroscopy. Using optical integral field spectra from the PHANGS-MUSE project, we identify SNRs in 19 nearby galaxies at ~ 100~pc scales. We use five different optical diagnostics: (1) line ratio maps of [SII]/H$\alpha$; (2) line ratio maps of [OI]/H$\alpha$; (3) velocity dispersion map of the gas; (4) and (5) two line ratio diagnostic diagrams from BPT diagrams to identify and distinguish SNRs from other nebulae. Given that our SNRs are seen in projection against HII regions and diffuse ionized gas, in our line ratio maps we use a novel technique to search for objects with [SII]/H$\alpha$ or [OI]/H$\alpha$ in excess of what is expected at fixed H$\alpha$ surface brightness within photoionized gas. In total, we identify 2,233 objects using at least one of our diagnostics, and define a subsample of 1,166 high-confidence SNRs that have been detected with at least two diagnostics. The line ratios of these SNRs agree well with the MAPPINGS shock models, and we validate our technique using the well-studied nearby galaxy M83, where all SNRs we found are also identified in literature catalogs and we recover 51% of the known SNRs. The remaining 1,067 objects in our sample are detected with only one diagnostic and we classify them as SNR candidates. We find that ~ 35% of all our objects overlap with the boundaries of HII regions from literature catalogs, highlighting the importance of using indicators beyond line intensity morphology to select SNRs. [OI]/H$\alpha$ line ratio is responsible for selecting the most objects (1,368; 61%), (abridged).