From core collapse to superluminous: the rates of massive stellar explosions from the Palomar Transient Factory

C. Frohmaier, C. R. Angus, M. Vincenzi, Mark Sullivan, M. Smith, P. E. Nugent, S. B. Cenko, A. Gal-Yam, S. R. Kulkarni, N. M. Law, R. M. Quimby

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We present measurements of the local core-collapse supernova (CCSN) rate using SN discoveries from the Palomar Transient Factory (PTF). We use a Monte Carlo simulation of hundreds of millions of SN light-curve realizations coupled with the detailed PTF survey detection efficiencies to forward model the SN rates in PTF. Using a sample of 86 CCSNe, including 26 stripped-envelope SNe (SESNe), we show that the overall CCSN volumetric rate is rCC v = 9.10 +1.56 −1.27 × 10−5 SNe yr−1 Mpc−3 h3 70 at z = 0.028, and the SESN volumetric rate is SE v = 2.41 +0.81 −0.64 × 10−5 SNe yr−1 Mpc−3 h3 70. We further measure a volumetric rate for hydrogen-free superluminous SNe (SLSNe-I) using eight events at z ≤ 0.2 of rSLSN−I v = 35 +25 −13 SNe yr−1 Gpc−3 h3 70, which represents the most precise SLSN-I rate measurement to date. Using a simple cosmic star formation history to adjust these volumetric rate measurements to the same redshift, we measure a local ratio of SLSN-I to SESN of ∼1/810+1500 −94 , and of SLSN-I to all CCSN types of ∼1/3500 +2800 −720 . However, using host galaxy stellar mass as a proxy for metallicity, we also show that this ratio is strongly metallicity dependent: in low-mass (logM < 9.5 M) galaxies, which are the only environments that host SLSN-I in our sample, we measure an SLSN-I to SESN fraction of 1/300 +380 −170 and 1/1700 +1800 −720 for all CCSN. We further investigate the SN rates a function of host galaxy stellar mass, and show that the specific rates of all CCSNe decrease with increasing stellar mass.
Original languageEnglish
Pages (from-to)5142-5148
Number of pages17
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
Early online date21 Nov 2020
Publication statusPublished - 1 Feb 2021


  • UKRI
  • STFC
  • ST/P006760/1
  • methods: data analysis
  • transients: supernovae


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