2026-10-15T06:09:47Z https://data.mendeley.com/oai

oai:data.mendeley.com/9fjyb5hyxt.1 2022-05-12T08:49:50Z

Ballantyne, John Tree boosting for learning EFT parameters Mendeley Data 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. Computational Physics Machine Learning Effective Field Theory Physics beyond the Standard Model Chatterjee, Suman Frohner, Nikolaus Lechner, Lukas Schöfbeck, Robert Schwarz, Dennis Dataset doi:10.17632/9fjyb5hyxt.1 oai:data.mendeley.com/9fjyb5hyxt.1 GNU Public License Version 3 http://www.gnu.org/licenses/gpl-3.0.en.html https://data.mendeley.com/datasets/9fjyb5hyxt 2022-05-12T08:49:50Z