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To draw inferences about gamma-ray burst (GRB) source populations based onSwift observations, it is essential to understand the detection efficiency ofthe Swift burst alert telescope (BAT). This study considers the problem ofmodeling the Swift/BAT triggering algorithm for long GRBs, a computationallyexpensive procedure, and models it using machine learning algorithms. A largesample of simulated GRBs from Lien 2014 is used to train various models: randomforests, boosted decision trees (with AdaBoost), support vector machines, andartificial neural networks. The best models have accuracies of $\gtrsim97\%$($\lesssim 3\%$ error), which is a significant improvement on a cut in GRB fluxwhich has an accuracy of $89.6\%$ ($10.4\%$ error). These models are then usedto measure the detection efficiency of Swift as a function of redshift $z$,which is used to perform Bayesian parameter estimation on the GRB ratedistribution. We find a local GRB rate density of $n_0 \sim0.48^{+0.41}_{-0.23} \ {\rm Gpc}^{-3} {\rm yr}^{-1}$ with power-law indices of$n_1 \sim 1.7^{+0.6}_{-0.5}$ and $n_2 \sim -5.9^{+5.7}_{-0.1}$ for GRBs aboveand below a break point of $z_1 \sim 6.8^{+2.8}_{-3.2}$. This methodology isable to improve upon earlier studies by more accurately modeling Swiftdetection and using this for fully Bayesian model fitting. The code used inthis is analysis is publicly available online(https://github.com/PBGraff/SwiftGRB_PEanalysis).

Machine Learning Model of the Swift/BAT Trigger Algorithm for Long GRB Population Studies