{
  "id": "2001.05967",
  "version": "v1",
  "published": "2020-01-16T18:01:13.000Z",
  "updated": "2020-01-16T18:01:13.000Z",
  "title": "Delay Time Distributions of Type Ia Supernovae From Galaxy and Cosmic Star Formation Histories",
  "authors": [
    "Louis-Gregory Strolger",
    "Steven A. Rodney",
    "Camilla Pacifici",
    "Gautham Narayan",
    "Or Graur"
  ],
  "comment": "20 pages, including 14 figures",
  "categories": [
    "astro-ph.GA",
    "astro-ph.CO",
    "astro-ph.HE"
  ],
  "abstract": "\\begin{abstract} We present analytical reconstructions of type Ia supernova (SN~Ia) delay time distributions (DTDs) by way of two independent methods: by a Markov chain Monte Carlo best-fit technique comparing the volumetric SN~Ia rate history to today's compendium cosmic star-formation history, and secondly through a maximum likelihood analysis of the star formation rate histories of individual galaxies in the GOODS/CANDELS field, in comparison to their resultant SN~Ia yields. {We adopt a flexible skew-normal DTD model, which could match a wide rage of physically-motivated DTD forms.} We find a family of solutions that are essentially exponential DTDs, similar in shape to the $\\beta\\approx-1$ power-law DTDs, but with more delayed events ($>1$ Gyr in age) than prompt events ($<1$ Gyr). {Comparing these solutions to delay time measures separately derived from field galaxies and galaxy clusters, we fined the skew-normal solutions can accommodate both without requiring a different DRTD form in different environments}. These model {fits} are generally inconsistent with results from single-degenerate binary populations synthesis models, and are seemingly supportive of double-degenerate progenitors for most SN~Ia events.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-01-16T18:01:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "cosmic star formation histories",
      "type ia supernova",
      "delay time distributions",
      "compendium cosmic star-formation history",
      "binary populations synthesis models"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 20,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}