On the detection of very high redshift Gamma Ray Bursts with Swift

TL;DR

This paper models the likelihood of detecting high-redshift Gamma Ray Bursts with Swift, showing that a significant fraction of detected bursts could be at z>5, and proposes a method to identify these high-redshift candidates efficiently.

Contribution

It introduces a probabilistic model for high-redshift GRB detection with Swift and develops a practical approach to identify z>5 GRB candidates in real-time.

Findings

Probability of detecting z>5 GRBs exceeds 10% for certain flux levels.

Estimated 10%-30% of Swift GRBs could be at z>5.

Method successfully identified high-redshift candidates with confirmed cases.

Abstract

We compute the probability to detect long Gamma Ray Bursts (GRBs) at z>5 with Swift, assuming that GRBs form preferentially in low-metallicity environments. The model fits well both the observed BATSE and Swift GRB differential peak flux distribution and is consistent with the number of z>2.5 detections in the 2-year Swift data. We find that the probability to observe a burst at z>5 becomes larger than 10% for photon fluxes P<1 ph s^{-1} cm^{-2}, consistent with the number of confirmed detections. The corresponding fraction of z>5 bursts in the Swift catalog is ~10%-30% depending on the adopted metallicity threshold for GRB formation. We propose to use the computed probability as a tool to identify high redshift GRBs. By jointly considering promptly-available information provided by Swift and model results, we can select reliable z>5 candidates in a few hours from the BAT detection. We test the procedure against last year Swift data: only three bursts match all our requirements, two being confirmed at z>5. Other three possible candidates are picked up by slightly relaxing the adopted criteria. No low-z interloper is found among the six candidates.