Modeling the Multi-band Light Curves of the Afterglows of Three Gamma-Ray Bursts and Their Associated Supernovae

TL;DR

This study introduces a multi-band broken power-law plus $^{56}$Ni model to fit the light curves of GRB-associated supernovae, effectively handling sparse data and providing more accurate $^{56}$Ni mass estimates than previous methods.

Contribution

The paper presents a novel multi-band fitting model for GRB-SNe light curves that works with limited data and yields higher, potentially more accurate $^{56}$Ni masses.

Findings

Model successfully fits multi-band light curves of three GRB-SNe.

Derived $^{56}$Ni masses are higher than previous estimates.

Model is reliable except for late-time $z$-band discrepancies.

Abstract

There are some dozen supernovae (SNe) associated with long Gamma-ray bursts (GRBs) have been confirmed. Most of previous studies derive the physical properties of the GRB-SNe by fitting the constructed (psuedo-)bolometric light curves. However, many GRB-SNe have only a few filter data, for which the (psuedo-)bolometric light curves are very difficult to be constructed. Additionally, constructing (psuedo-)bolometric light curves rely on some assumptions. In this paper, we use the multi-band broken power-law plus $^{56}$Ni model to fit the multi-band light curves of the afterglows and the SNe (SN~2001ke, SN~2013dx, and SN~2016jca) associated with three GRBs (GRB~011121, GRB~130702A, and GRB~161219B). We find our model can account for the multi-band light curves of the three GRB-SNe (except for the late-time $z-$band light curves of two events), indicating that the model is a reliable model. The $^{56}$Ni masses we derive are higher than that in the literature. This might be due to the fact that the $^{56}$Ni masses in the literature are usually obtained by fitting the psuedo-bolometric light curves whose luminosities are usually (significantly) underestimated. We suggest that the multi-band model can not only be used to fit the multi-band light curves of GRB-SNe that have many filter observations, but also fit those having sparse data.