2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Language-Guided Hypotheses Generation for Sparse SMEFT Analyses
Ahmed Hammad, Veronica Sanz
Global fits of the Standard Model Effective Field Theory are challenged by the large number of operators, while any given database constrains only a small subset. Selecting relevan…
Machine-Learning-Inspired SMEFT Simplified Template Cross Sections: A Case Study in ZH Production
Daniel Conde, Miguel G. Folgado, Veronica Sanz
The Simplified Template Cross Section (STXS) program has become the standard interface between Higgs measurements and global fits, but its fixed one-dimensional boundaries are not…
Angular Coefficients from Interpretable Machine Learning with Symbolic Regression
Josh Bendavid, Daniel Conde, Manuel Morales-Alvarado +2
We explore the use of symbolic regression to derive compact analytical expressions for angular observables relevant to electroweak boson production at the Large Hadron Collider (LH…
Symbolic regression for precision LHC physics
Manuel Morales-Alvarado, Daniel Conde, Josh Bendavid +2
We study the potential of symbolic regression (SR) to derive compact and precise analytic expressions that can improve the accuracy and simplicity of phenomenological analyses at t…