Work

Learning the EFT likelihood with tree boosting

Year: 2022
Type: preprint
Abstract: We develop a tree boosting algorithm for collider measurements of multiple Wilson coefficients in effective field theories describing phenomena beyond the standard model of particle physics. The design of the discriminant exploits per-event information of the simulated data sets that encodes the predictions for different values of the Wilson coefficients. This ``Boosted Information Tree'' algorithm provides nearly optimal discrimination power order-by-order in the expansion in the Wilson coefficients and approaches the optimal likelihood ratio test statistic. As a proof-of-principle, we apply the algorithm to the $\textrm{pp}\rightarrow\textrm{Zh}$ process for different types of modeling.
Language: English
Open Access status: green
OpenAlex ID: W4282004305
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Cited by: 4
Related to: 10