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Tree boosting for learning EFT parameters
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. W...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
2021
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2776940 |
Sumario: | 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. |
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