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• W3183439224 startingPage "108385" @default.
• W3183439224 abstract "We present a new tree boosting algorithm designed for the measurement of parameters in the context of effective field theory (EFT). To construct the algorithm, we interpret the optimized loss function of a traditional decision tree as the maximal Fisher information in Poisson counting experiments. We promote the interpretation to general EFT predictions and develop a suitable boosting method. The resulting ``Boosted Information Tree'' algorithm approximates the score, the derivative of the log-likelihood function with respect to the parameter. It thus provides a sufficient statistic in the vicinity of a reference point in parameter space where the estimator is trained. The training exploits per-event information of likelihood ratios for different theory parameter values available in the simulated EFT data sets." @default.
• W3183439224 created "2021-08-02" @default.
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• W3183439224 date "2022-08-01" @default.
• W3183439224 modified "2023-10-13" @default.
• W3183439224 title "Tree boosting for learning EFT parameters" @default.
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• W3183439224 doi "https://doi.org/10.1016/j.cpc.2022.108385" @default.
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