Abstract

Contributed Talk - Splinter SNR

Thursday, 12 September 2024, 15:15   (S14)

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

Jing Li, K. Kreckel, S. Sarbadhicary, Oleg V. Egorov, B. Groves, K. S. Long, Enrico Congiu, Francesco Belfiore, Simon C. O. Glover, Ashley . T Barnes, Frank Bigiel, Guillermo A. Blanc, Kathryn Grasha, Ralf S. Klessen, Adam Leroy, Laura A. Lopez, J. Eduardo Méndez-Delgado, Justus Neumann, Eva Schinnerer, Thomas G. Williams, PHANGS collaborators
1 Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstraße 12-14, 69120 Heidelberg, Germany 2 Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, Ohio 43210, USA 3 International Centre for Radio Astronomy Research, University of Western Australia, 7 Fairway, Crawley, 6009 WA, Australia 4 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA 5 European Southern Observatory (ESO), Alonso de Córdova 3107, Casilla 19, Santiago 19001, Chile 6 INAF – Arcetri Astrophysical Observatory, Largo E. Fermi 5, I-50125, Florence, Italy 7 Zentrum für Astronomie, Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, 69120 Heidelberg, Germany 8 European Southern Observatory (ESO), Karl-Schwarzschild-Straße 2, 85748 Garching, Germany 9 Argelander-Institut für Astronomie, Universität Bonn, Auf dem Hügel 71, 53121 Bonn, Germany 10 Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101, USA 11 Departamento de Astronomía, Universidad de Chile, Camino del Observatorio 1515, Las Condes, Santiago, Chile 12 Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia 13 Universität Heidelberg, Interdisziplinäres Zentrum für Wissenschaftliches Rechnen, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany 14 Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg, Germany 15 Sub-department of Astrophysics, Department of Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK

We present the largest extragalactic survey of supernova remnant (SNR) candidates in nearby star-forming galaxies using exquisite spectroscopic maps from MUSE. Supernova remnants (SNRs) 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]/Hα; (2) line ratio maps of [OI]/Hα; (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]/Hα or [OI]/Hα in excess of what is expected at fixed Hα 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 H ii regions from literature catalogs, highlighting the importance of using indicators beyond line intensity morphology to select SNRs. We find that the [OI]/Hα line ratio is responsible for selecting the most objects (1,368; 61%), however, only half are classified as SNRs, demonstrating how the use of multiple diagnostics is key to both increasing our sample size and improving our confidence in our SNR classifications.