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RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP

Abstract

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 these training data suggest that {approx}56% of high-z bursts can be captured from following up the  More>>
Authors:
Morgan, A N; Richards, Joseph W; Butler, Nathaniel R; Bloom, Joshua S; [1]  Long, James; Broderick, Tamara [2] 
  1. Department of Astronomy, University of California, Berkeley, CA 94720-3411 (United States)
  2. Department of Statistics, University of California, Berkeley, CA 94720-3860 (United States)
Publication Date:
Feb 20, 2012
Product Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 746; Journal Issue: 2; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; COSMIC GAMMA BURSTS; DATA ANALYSIS; RANDOMNESS; RED SHIFT; TELESCOPES
OSTI ID:
22011675
Country of Origin:
United States
Language:
English
Other Identifying Numbers:
Journal ID: ISSN 0004-637X; CODEN: ASJOAB; TRN: US12Q7545098776
Availability:
Available from http://dx.doi.org/10.1088/0004-637X/746/2/170
Submitting Site:
USN
Size:
[16 page(s)]
Announcement Date:
Jan 03, 2013

Citation Formats

Morgan, A N, Richards, Joseph W, Butler, Nathaniel R, Bloom, Joshua S, Long, James, and Broderick, Tamara. RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP. United States: N. p., 2012. Web. doi:10.1088/0004-637X/746/2/170.
Morgan, A N, Richards, Joseph W, Butler, Nathaniel R, Bloom, Joshua S, Long, James, & Broderick, Tamara. RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP. United States. doi:10.1088/0004-637X/746/2/170.
Morgan, A N, Richards, Joseph W, Butler, Nathaniel R, Bloom, Joshua S, Long, James, and Broderick, Tamara. 2012. "RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP." United States. doi:10.1088/0004-637X/746/2/170. https://www.osti.gov/servlets/purl/10.1088/0004-637X/746/2/170.
@misc{etde_22011675,
title = {RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP}
author = {Morgan, A N, Richards, Joseph W, Butler, Nathaniel R, Bloom, Joshua S, Long, James, and Broderick, Tamara}
abstractNote = {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 these training data suggest that {approx}56% of high-z bursts can be captured from following up the top 20% of the ranked candidates, and {approx}84% of high-z bursts are identified after following up the top {approx}40% of candidates. We further use the method to rank 200 + Swift bursts with unknown redshifts according to their likelihood of being high-z.}
doi = {10.1088/0004-637X/746/2/170}
journal = {Astrophysical Journal}
issue = {2}
volume = {746}
journal type = {AC}
place = {United States}
year = {2012}
month = {Feb}
}