SO Colloquium – New optical diagnostics and Machine Learning classification of emission-line nebulae: Toward larger and more complete samples of supernova remnants in nearby galaxies

Supernova remnants (SNRs) provide valuable insights into supernova (SN) environments and the long-term influence of SN-driven shocks on the interstellar medium (ISM). By heating, compressing, and chemically enriching the ISM, SNRs trace how different explosion environments shape galaxy evolution. However, classical optical criteria, particularly the [S II]/Hα ratio, miss a significant fraction of low-excitation or evolved SNRs, biasing current extragalactic SNR samples.

To mitigate these biases, we develop new optical diagnostics based on two- and three-line emission ratios combined with a Support Vector Machine classifier. This method reliably separates SNRs from contaminating H II regions and recovers up to ~35% of remnants overlooked by traditional cuts, enabling more complete SNR samples.

We apply these tools to MUSE IFU observations of nearby galaxies, identifying previously unrecognized SNR populations and characterizing their physical properties and luminosity functions. Using complementary Chandra observations, we also detect new X-ray SNRs and investigate links between their optical and X-ray emission. We find that remnants located in higher pre-shock densities tend to be more X-ray luminous, and that optical SNRs with X-ray counterparts commonly show enhanced [O III] λ5007 emission, consistent with faster shock velocities.

Finally, we present extended diagnostics that incorporate a broader set of emission lines and physical parameters, enabling the robust identification and classification of emission-line nebulae, including SNRs, H II regions, and planetary nebulae. These tools provide a general framework for detecting and characterizing previously unrecognized nebular populations in nearby galaxies.

Fecha y lugar: 19/03/2026 – 12:30 | Salón de Actos
Maria Kopsacheili
ICE-CSIC