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Title: DES14X3taz: A Type I superluminous supernova showing a luminous, rapidly cooling initial pre-peak bump

Journal Article · · The Astrophysical Journal. Letters (Online)
 [1]
  1. Univ. of Southampton, Southampton (United Kingdom). et al.

© 2016. The American Astronomical Society. All rights reserved. We present DES14X3taz, a new hydrogen-poor superluminous supernova (SLSN-I) discovered by the Dark Energy Survey (DES) supernova program, with additional photometric data provided by the Survey Using DECam for Superluminous Supernovae. Spectra obtained using Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy on the Gran Telescopio CANARIAS show DES14X3taz is an SLSN-I at z = 0.608. Multi-color photometry reveals a double-peaked light curve: a blue and relatively bright initial peak that fades rapidly prior to the slower rise of the main light curve. Our multi-color photometry allows us, for the first time, to show that the initial peak cools from 22,000 to 8000 K over 15 rest-frame days, and is faster and brighter than any published core-collapse supernova, reaching 30% of the bolometric luminosity of the main peak. No physical 56 Ni-powered model can fit this initial peak. We show that a shock-cooling model followed by a magnetar driving the second phase of the light curve can adequately explain the entire light curve of DES14X3taz. Models involving the shock-cooling of extended circumstellar material at a distance of ≃400 R⊙ are preferred over the cooling of shock-heated surface layers of a stellar envelope. We compare DES14X3taz to the few double-peaked SLSN-I events in the literature. Although the rise times and characteristics of these initial peaks differ, there exists the tantalizing possibility that they can be explained by one physical interpretation.

Research Organization:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Contributing Organization:
The DES Collaboration; DES Collaboration
Grant/Contract Number:
AC02-76SF00515; AC02-07CH11359; AC02-05CH11231
OSTI ID:
1257728
Alternate ID(s):
OSTI ID: 1244523; OSTI ID: 1393027
Report Number(s):
SLAC-PUB-16570; FERMILAB-PUB-15-577-AE; arXiv:1512.06043
Journal Information:
The Astrophysical Journal. Letters (Online), Vol. 818, Issue 1; ISSN 2041-8213
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 63 works
Citation information provided by
Web of Science

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The Dark Energy Survey: more than dark energy – an overview journal March 2016
Cosmic evolution and metal aversion in superluminous supernova host galaxies journal September 2017
The GRB–SLSN connection: misaligned magnetars, weak jet emergence, and observational signatures journal January 2018
Rapidly evolving transients in the Dark Energy Survey journal August 2018
Superluminous supernovae from the Dark Energy Survey journal May 2019
On the Early-Time Excess Emission in Hydrogen-Poor Superluminous Supernovae journal January 2017
Analyzing the Largest Spectroscopic Data Set of Hydrogen-poor Super-luminous Supernovae journal August 2017
Hydrogen-poor Superluminous Supernovae with Late-time H α Emission: Three Events From the Intermediate Palomar Transient Factory journal October 2017
The Magnetar Model for Type I Superluminous Supernovae. I. Bayesian Analysis of the Full Multicolor Light-curve Sample with MOSFiT journal November 2017
Hydrogen-poor Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey journal January 2018
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Light Curves of Hydrogen-poor Superluminous Supernovae from the Palomar Transient Factory journal June 2018
Systematic Investigation of the Fallback Accretion-powered Model for Hydrogen-poor Superluminous Supernovae journal November 2018
Superluminous Supernovae in LSST: Rates, Detection Metrics, and Light-curve Modeling journal December 2018
A Systematic Study of Superluminous Supernova Light-curve Models Using Clustering journal March 2019
First Release of High-redshift Superluminous Supernovae from the Subaru HIgh-Z SUpernova CAmpaign (SHIZUCA). II. Spectroscopic Properties journal March 2019
Cosmic evolution and metal aversion in superluminous supernova host galaxies text January 2018
SN 2015bn: A DETAILED MULTI-WAVELENGTH VIEW of A NEARBY SUPERLUMINOUS SUPERNOVA journalarticle January 2016
Models and simulations for the photometric lsst astronomical time series classification challenge (Plasticc) text January 2019
The Dark Energy Survey: more than dark energy - an overview text January 2016
SN 2015bn: a detailed multi-wavelength view of a nearby superluminous supernova text January 2016
Analyzing the Largest Spectroscopic Dataset of Hydrogen-Poor Super-Luminous Supernovae text January 2016
Hydrogen-poor Superluminous Supernovae With Late-time H-alpha Emission: Three Events From the Intermediate Palomar Transient Factory text January 2017
The GRB-SLSN Connection: mis-aligned magnetars, weak jet emergence, and observational signatures text January 2017
The magnetar model for Type I superluminous supernovae I: Bayesian analysis of the full multicolour light curve sample with MOSFiT text January 2017
Hydrogen-Poor Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey text January 2017
A statistical approach to identify superluminous supernovae and probe their diversity text January 2017
First release of high-redshift superluminous supernovae from the Subaru HIgh-Z sUpernova CAmpaign (SHIZUCA). II. Spectroscopic properties text January 2018
Rapidly evolving transients in the Dark Energy Survey text January 2018
A nearby superluminous supernova with a long pre-maximum 'plateau' and strong CII features text January 2018
Superluminous Supernovae from the Dark Energy Survey text January 2018
A Systematic Study Of Superluminous Supernova Lightcurve Models Using Clustering text January 2019

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