Year: 2026
Type: preprint
Abstract: Over its five-year campaign (2013-2017), the Dark Energy Survey (DES) supernova (SN) survey recorded 31,636 light curves. Using a custom-built, two-stage random forest classifier, we identified 2,445 likely core-collapse SNe (CC SNe) within the survey rate analysis footprint. With this sample, we measure the volumetric CC SN rate from redshift z = 0 to 1.3 in 11 redshift bins, representing the most detailed single-survey coverage to date across nine billion years of lookback time. Over this interval, the CC SN rates are well fit by a power law R_CC(z) = R_0 (1 + z)^α, with R_0 = 1.58 \pm 0.17 \times 10^{-4} \text{ yr}^{-1} \text{ Mpc}^{-3} and α= 2.85 \pm 0.25. The rate turnover, expected at cosmic noon, is not yet apparent. Because CC SNe trace recent star formation, we compare our measurements with recent cosmic star-formation history (CSFH) models. Our measurements are ~70 per cent higher than CSFH models using a Salpeter initial mass function (IMF) to predict CC SN rates. Replacing the Salpeter IMF with a Baldry and Glazebrook IMF and a CC SN progenitor mass range of 8-40 M_\odot bridges the gap. Alternatively, CSFHs may miss a hitherto unidentified population of low-luminosity galaxies. We further find that the local ratio of stripped-envelope (SE) SNe to all CC SNe is 31 \pm 5 per cent, remaining constant from z = 0 to 1.3. Combined with an IMF analysis accounting for the "missing progenitor problem" of hydrogen-rich SNe, our results favour a mixture of single massive stars and binaries as SE SN progenitors, with the redshift-invariant ratio favouring binary channels.
Source: arXiv (Cornell University)
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, University of Pennsylvania, Ohio State University, University of Chicago, University of Portsmouth, Centres de Recerca de Catalunya, University College London, European Commission, University of Sussex, Deutsche Forschungsgemeinschaft, Generalitat de Catalunya, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Financiadora de Estudos e Projetos, Ministerio de Ciencia e Innovación, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Science and Technology Facilities Council, European Regional Development Fund, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
Awards: G1054097061, G1249197737, G1328166135, G1491530973, G1682672702, G1993551506, G265847165, G3154116105, G3467815775, G3586607516, G3715052068, G3740091667, The Dark Energy Survey Data Management Operations, G3968781304, G419118642, G4334057779, G454676879, G490073602, G496129201, G5617605617, G6480793128, G6514847661, G6619390584, G6636566961, G6676276723, G6731467812, G7290792616, G7711421660, G7788408655, G7835623888, G7954596376, Collaborative Research: The Dark Energy Survey Data Management Operations
OpenAlex ID: W7223099124