flavour anomalies 1global fits 1gradient boosted trees 1likelihood emulation 1shap interpretability 1
From the 1 of 3 linked papers with an AI index.
3 papers
hep-ph2026
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
Jorge Alda, Jacobo Asorey, Alejandro Mir +1
The paper presents a machine‑learning framework using gradient‑boosted regression trees (XGBoost) to emulate complex, non‑Gaussian likelihoods for global fits in high‑energy physic…
hep-ph2026
Flavour Anomalies: A comparative analysis using a machine learning algorithm
Jorge Alda, Alejandro Mir, Siannah Penaranda
We present an analysis on flavour anomalies in semileptonic rare -meson decays using an effective field theory approach and assuming that new physics affects only one generation…
hep-ph2025
B-Meson Anomalies: Effective Field Theory Meets Machine Learning
Alejandro Mir, Jorge Alda, Siannah Penaranda
Discrepancies between experimental measurements and Standard Model predictions in -meson decays, especially in lepton flavor universality ratios like , a…