arXiv subjects

Alejandro Mir

Publications and source records attributed to Alejandro Mir.

3 recordsLinked to original sources

arXiv2026

Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology

Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian likelihood landscapes using gradient-boosted regression trees (XGBoost). We discuss the advantages of the Machine Learning approach in terms of computational efficiency and the resolution of confidence regions, particularly in scenarios with complex correlations or "curved" degeneracies. We validate this methodology by applying it to a recent analysis on flavour anomalies in semileptonic $B$ meson decays and discussing the adaptability of this framework to other phenomenological systems, such as axion-like particles or cosmology global fits. Finally, we utilise SHAP (Shapley Additive exPlanations) values to provide a transparent analysis of feature importance, ensuring that the Machine Learning predictions remain physically interpretable and consistent with the underlying physics.

hep-ph↗

arXiv2025

B-Meson Anomalies: Effective Field Theory Meets Machine Learning

Discrepancies between experimental measurements and Standard Model predictions in $B$-meson decays, especially in lepton flavor universality ratios like $R_{D^{(*)}}$, $R_{J/\psi}$ and branching ratios for processes like $B\to K^+\nu\bar\nu$, suggest possible new physics (NP). In this study, we use an effective field theory framework, assuming NP effects only affect a single generation in the interaction basis, leading to non-universal mixing when rotating to the mass basis. We perform a global fit to the current experimental data, exploring three scenarios characterized by different mixing patterns and constraints. Our analysis finds that the best fit involves mixing between the second and third quark generations, with no lepton sector mixing and independent coefficients for singlet and triplet four-fermion operators. To accurately capture the non-Gaussian nature of the resulting parameter distributions, we use a Machine Learning-based Monte Carlo algorithm, enabling the generation of representative samples that reflect the true underlying distributions. This work highlights the valuable role of Machine Learning in accurately modeling complex parameter distributions in particle physics analyses.

hep-ph↗

arXiv2024

Flavour Anomalies: A comparative analysis using a machine learning algorithm

We present an analysis on flavour anomalies in semileptonic rare $B$-meson decays using an effective field theory approach and assuming that new physics affects only one generation in the interaction basis and non-universal mixing effects are generated by the rotation to the mass basis. A global fit to experimental data is performed, focusing on LFU ratios $R_{D^{(*)}}$ and $R_{J/\psi}$ and branching ratios that exhibit tensions with Standard Model predictions on $B \rightarrow K^{(*)} \nu \bar{\nu}$ decays. In our analysis, we use a Machine Learning Montecarlo algorithm, a framework that emulates the highly non-Gaussian structure of the likelihood landscape with minimal training cost. This method enables the generation of high-resolution confidence regions and detailed correlation analyses. By comparing three different scenarios, we show that the one that introduces only mixing between the second and third quark generations and no mixing in the lepton sector, as well as independent coefficients for the singlet and triplet four fermion effective operators, provides the best fit to the experimental data. A comparison with previous results is performed. We highlight the key strengths of the Machine Learning framework in our analysis.

hep-ph↗