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 resembling a GRB-SN. We derive/collect the basic physical parameters of the GRBs and SNe, and look for correlations among these parameters. We find that the peak brightness, 56 Ni mass, and explosion energy of SNe associated with GRBs are statistically higher than other Type Ib/c SNe. A statistically significant relation between the peak energy of GRBs and the peak brightness of their associated SNe is confirmed. No significant correlations are found between the GRB energies (either isotropic or beaming-corrected) and the supernova energy. We investigate the energy partition within these systems and find that the beaming-corrected GRB energy of most systems is smaller than the SN energy, with less than 30% of the total energy distributed in the relativistic jet. The total energy of the systems is typically smaller than the maximum available energy of a millisecond magnetar (2 × 10 52 erg), especially if aspherical SN explosions are considered. The data are consistent with-though not proof of-the hypothesis that most, but not all, GRB/SN systems are powered by millisecond magnetars.

The effects of a magnetar engine on the gamma-ray burst-associated supernovae: Application to double-peaked SN 2006aj

“…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.…”

“…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.…”

The Physics of Star Cluster Formation and Evolution

“…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).…”

Stripped-envelope core-collapse supernova $^{56}$Ni masses: Persistently larger values than supernovae type II

“…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 .…”