Search for the Signatures of a New-Born Black Hole from the Collapse of a Supra-massive Millisecond Magnetar in Short GRB Light Curves

TL;DR

This paper investigates the signatures of newly formed black holes from magnetar collapse in short GRB light curves, proposing a model involving magnetic flux and accretion processes to explain observed X-ray features.

Contribution

The study introduces a model linking magnetar collapse, magnetic flux accumulation, and accretion disk dynamics to explain distinct X-ray light curve types in short GRBs.

Findings

Identified two types of X-ray light curves consistent with the model.

Found two candidate GRBs matching the predicted light curve signatures.

Model successfully explains observed GRB light curve features.

Abstract

`Internal plateau' followed by a sharp decay is commonly seen in short gamma-ray burst (GRB) light curves. The plateau component is usually interpreted as the dipole emission from a supra-massive magnetar, and the sharp decay may imply the collapse of the magnetar to a black hole (BH). Fall-back accretion onto the new-born BH could produce long-lasting activities via the Blandford-Znajek (BZ) process. The magnetic flux accumulated near the BH would be confined by the accretion disks for a period of time. As the accretion rate decreases, the magnetic flux is strong enough to obstruct gas infall, leading to a magnetically-arrested disk (MAD). Within this scenario, we show that the BZ process could produce two types of typical X-ray light curves: type I exhibits a long-lasting plateau, followed by a power-law decay with slopes ranging from 5/3 to 40/9; type II shows roughly a single power-law decay with slope of 5/3. The former requires low magnetic filed strength, while the latter corresponds to relatively high values. We search for such signatures of the new-born BH from a sample of short GRBs with an internal plateau, and find two candidates: GRB 101219A and GRB 160821B, corresponding to type II and type I light curve, respectively. It is shown that our model can explain the data very well.