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Modeling the Swift Bat Trigger Algorithm with Machine LearningTo draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.
Document ID
20170003289
Document Type
Reprint (Version printed in journal)
Authors
Graff, Philip B. (Maryland Univ. College Park, MD, United States)
Lien, Amy Y. (Maryland Univ. Baltimore County Baltimore, MD, United States)
Baker, John G. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Sakamoto, Takanori (Aoyamagakuin Univ. Kanagawa, Japan)
Date Acquired
April 7, 2017
Publication Date
February 8, 2016
Publication Information
Publication: The Astrophysical Journal
Volume: 818
Issue: 1
ISSN: 2041-8205
Subject Category
Astrophysics
Astronomy
Report/Patent Number
GSFC-E-DAA-TN41331
Funding Number(s)
CONTRACT_GRANT: NNG17PT01A
CONTRACT_GRANT: NNG06EO90A
Distribution Limits
Public
Copyright
Other
Keywords
gamma-ray burst: general – gamma-rays: general – methods: data analysis