{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-model-of-the-swiftbat","title":"Machine Learning Model of the Swift/BAT Trigger Algorithm for Long GRB Population Studies","arxiv_id":"1509.01228","date":"2015-09-03","proceeding":null,"authors":["Philip B Graff","Amy Y Lien","John G Baker","Takanori Sakamoto"],"abstract":"To draw inferences about gamma-ray burst (GRB) source populations based on\nSwift observations, it is essential to understand the detection efficiency of\nthe Swift burst alert telescope (BAT). This study considers the problem of\nmodeling the Swift/BAT triggering algorithm for long GRBs, a computationally\nexpensive procedure, and models it using machine learning algorithms. A large\nsample of simulated GRBs from Lien 2014 is used to train various models: random\nforests, boosted decision trees (with AdaBoost), support vector machines, and\nartificial neural networks. The best models have accuracies of $\\gtrsim97\\%$\n($\\lesssim 3\\%$ error), which is a significant improvement on a cut in GRB flux\nwhich has an accuracy of $89.6\\%$ ($10.4\\%$ error). These models are then used\nto measure the detection efficiency of Swift as a function of redshift $z$,\nwhich is used to perform Bayesian parameter estimation on the GRB rate\ndistribution. We find a local GRB rate density of $n_0 \\sim\n0.48^{+0.41}_{-0.23} \\ {\\rm Gpc}^{-3} {\\rm yr}^{-1}$ with power-law indices of\n$n_1 \\sim 1.7^{+0.6}_{-0.5}$ and $n_2 \\sim -5.9^{+5.7}_{-0.1}$ for GRBs above\nand below a break point of $z_1 \\sim 6.8^{+2.8}_{-3.2}$. This methodology is\nable to improve upon earlier studies by more accurately modeling Swift\ndetection and using this for fully Bayesian model fitting. The code used in\nthis is analysis is publicly available online\n(https://github.com/PBGraff/SwiftGRB_PEanalysis).","url_abs":"http://arxiv.org/abs/1509.01228v2","url_pdf":"http://arxiv.org/pdf/1509.01228v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-model-of-the-swiftbat","repo_url":"https://github.com/PBGraff/SwiftGRB_PEanalysis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}