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machine-z: rapid machine-learned redshift indicator for swift gamma-ray burstsStudies 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 approximately 100 per cent recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.
Document ID
20170003777
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Ukwatta, T. N.
(Los Alamos National Lab. NM, United States)
Wozniak, P. R.
(Los Alamos National Lab. NM, United States)
Gehrels, N.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
April 20, 2017
Publication Date
March 8, 2016
Publication Information
Publication: Monthly Notices of the Royal Astronomical Society
Volume: 458
Issue: 4
ISSN: 0035-8711
Subject Category
Astrophysics
Report/Patent Number
GSFC-E-DAA-TN41954
Distribution Limits
Public
Copyright
Other
Keywords
gamma-ray burst: general