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This content will become publicly available on March 8, 2017

Title: Machine-z: Rapid machine-learned redshift indicator for Swift gamma-ray bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce ‘machine-z’, a redshift prediction algorithm and a ‘high-z’ classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve ~100 per cent recall. As amore » result, the most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.« less
Authors:
 [1] ;  [1] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. NASA/Goddard Space Flight Center, Greenbelt, MD (United States)
Publication Date:
OSTI Identifier:
1246890
Report Number(s):
LA-UR--15-29685
Journal ID: ISSN 0035-8711
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 458; Journal Issue: 4; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Research Org:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS Astronomy and Astrophysics; GRB, Machine Learning