Tree boosting for learning EFT parameters

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.

23 pages, 3 figures. Updated with referee comments

References in corpus (4)

• Array Programming with NumPy
• Introduction to Effective Field Theory
• SMEFT analysis of vector boson scattering and diboson data from the LHC Run II
• Combined SMEFT interpretation of Higgs, diboson, and top quark data from the LHC

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