Learning the EFT likelihood with tree boosting

2022/05/25 by S. Chatterjee, Stefan Rohshap, Chatterjee, Suman +5 · 1 citation
Computer Science · Engineering · Mathematics · #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Nuclear reactor physics and engineering #Statistical Methods and Inference #VLSI and Analog Circuit Testing

paper · pdf · doi:10.48550/arxiv.2205.12976

openalex publication_date 2022/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 \textrmpp→\textrmZh process for different types of modeling.

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