2018
DOI: 10.3847/1538-4357/aacd03
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Gamma-Ray Burst/Supernova Associations: Energy Partition and the Case of a Magnetar Central Engine

Abstract: The favored progenitor model for Gamma-ray Bursts (GRBs) with Supernova (SN) association is the core collapse of massive stars. One possible outcome of such a collapse is a rapidly spinning, strongly magnetized neutron star ("magnetar"). We systematically analyze the multi-wavelength data of GRB/SN associations detected by several instruments before 2017 June. Twenty GRB/SN systems have been confirmed via direct spectroscopic evidence or a clear light curve bump, as well as some spectroscopic evidence resembli… Show more

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Cited by 27 publications
(29 citation statements)
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References 149 publications
(142 reference statements)
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“…On the other hand, although the GRBSNe are not expected to be as luminous as SLSNe 1 , their luminosities are still found to be somewhat higher than those of normal supernovae and thus a more powerful energy source is usually required. The most direct explanation of such an energy source is a relatively high mass of 56 Ni (Drout et al 2011;Lyman et al 2016;Prentice et al 2016;Lü et al 2018), which could in principle be synthesized during the shock acceleration of the SN ejecta due to the energy injection from the magnetar (Suwa & Tominaga 2015;Nishimura et al 2015). In summary, the basic features of GRBSNe can generally be consistent with the millisecond magnetar engine model.…”
Section: Introductionmentioning
confidence: 61%
See 1 more Smart Citation
“…On the other hand, although the GRBSNe are not expected to be as luminous as SLSNe 1 , their luminosities are still found to be somewhat higher than those of normal supernovae and thus a more powerful energy source is usually required. The most direct explanation of such an energy source is a relatively high mass of 56 Ni (Drout et al 2011;Lyman et al 2016;Prentice et al 2016;Lü et al 2018), which could in principle be synthesized during the shock acceleration of the SN ejecta due to the energy injection from the magnetar (Suwa & Tominaga 2015;Nishimura et al 2015). In summary, the basic features of GRBSNe can generally be consistent with the millisecond magnetar engine model.…”
Section: Introductionmentioning
confidence: 61%
“…Therefore, different from SLSNe, it is expected that GRBSNe cannot be brightened to be much more luminous than normal corecollapse supernovae and, instead, the majority of the spin-down energy is primarily converted into the kinetic energy of the supernova ejecta. In observations, since the discovery of the GRB 980425/SN 1998bw association event (Galama et al 1998;Iwamoto et al 1998), several tens of GRBSNe have been discovered (see, Hjorth & Bloom 2012;Cano et al 2017;Lü et al 2018 and the references therein). On the one hand, the spectral features of these GRBSNe indicate they belong to broad-lined Type Ic supernovae (SNe Ic-BL; Woosley & Bloom 2006;Cano et al 2017), which hints the GRBSNe are indeed given huge kinetic energy.…”
Section: Introductionmentioning
confidence: 99%
“…It is also possible to increase it by extreme assumptions on stellar feedback. If the most massive stars in a cluster exploded as hypernovae, all releasing ten times the conventional supernova energy output of 10 51 erg (e.g., Mazzali et al, 2014;Lü et al, 2018), this would increase the critical compactness index to C 5 ≈ 30. A more comprehensive analytic treatment by Matzner & Jumper (2015) that takes into account accretion and various feedback processes separately find the threshold at 3 km s −1 (C 5 = 0.2).…”
Section: Gas Expulsion In Massive Star Clustersmentioning
confidence: 99%
“…This is because these objects show the largest degree of asphericities among SE-SN (Maeda et al 2008), and their emission may also have a significant contribution from sources other than radioactive decay (e.g. Wang et al 2017;Lü et al 2018). To obtain peak luminosities we applied a local polynomial regression with a Gaussian kernel, using the public modules from PyQt-fit in Python 4 .…”
Section: Bolometric Light Curvesmentioning
confidence: 99%