17 citations · 40 across the 9 of their papers we have counts for
9 papers
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
Jorge Alda, Jacobo Asorey, Alejandro Mir +1
Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex lik…
Lecture notes on Machine Learning applications for global fits
Jorge Alda
These lecture notes provide a comprehensive framework for performing global statistical fits in high-energy physics using modern Machine Learning (ML) surrogates. We begin by revie…
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 , an…
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…
Exploring B-physics anomalies at colliders
Jorge Alda, Jaume Guasch, Siannah Penaranda
Several experimental measurements of meson decays, in tension with Standard Model predictions, exhibit large sources of Lepton Flavour Universality violation. We perform an ana…
Using Machine Learning techniques in phenomenological studies in flavour physics
Jorge Alda, Jaume Guasch, Siannah Penaranda
An updated analysis of New Physics violating Lepton Flavour Universality, by using the Standard Model Effective Field Lagrangian with semileptonic dimension six operators at $Λ= 1\…