Adam N. Morgan, James Long, Joseph W. Richards, Tamara Broderick, Nathaniel R. Butler, Joshua S. Bloom
As the number of observed Gamma-Ray Bursts (GRBs) continues to grow,
follow-up resources need to be used more efficiently in order to maximize
science output from limited telescope time. As such, it is becoming
increasingly important to rapidly identify bursts of interest as soon as
possible after the event, before the afterglows fade beyond detectability.
Studying the most distant (highest redshift) events, for instance, remains a
primary goal for many in the field. Here we present our Random forest Automated
Triage Estimator for GRB redshifts (RATE GRB-z) for rapid identification of
high-redshift candidates using early-time metrics from the three telescopes
onboard Swift. While the basic RATE methodology is generalizable to a number of
resource allocation problems, here we demonstrate its utility for
telescope-constrained follow-up efforts with the primary goal to identify and
study high-z GRBs. For each new GRB, RATE GRB-z provides a recommendation -
based on the available telescope time - of whether the event warrants
additional follow-up resources. We train RATE GRB-z using a set consisting of
135 Swift bursts with known redshifts, only 18 of which are z > 4.
Cross-validated performance metrics on this training data suggest that ~56% of
high-z bursts can be captured from following up the top 20% of the ranked
candidates, and ~84% of high-z bursts are identified after following up the top
~40% of candidates. We further use the method to rank 200+ Swift bursts with
unknown redshifts according to their likelihood of being high-z.
View original:
http://arxiv.org/abs/1112.3654
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