Year: 2026
Type: article
Abstract: A bstract 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 (LHC). Focusing on the angular coefficients that govern the decay distributions of W and Z bosons, we investigate whether symbolic models can well approximate these quantities, typically computed via computationally costly numerical procedures, with high fidelity and interpretability. Using the PySR package, we first validate the approach in controlled settings, namely in angular distributions in lepton-lepton collisions in QED and in leading-order Drell-Yan production at the LHC. We then apply symbolic regression to extract closed-form expressions for the angular coefficients A i as functions of transverse momentum, rapidity, and invariant mass, using next-to-leading order simulations of pp → ℓ + ℓ − events. Our results demonstrate that symbolic regression can produce accurate and generalisable expressions that match Monte Carlo predictions within uncertainties, while preserving interpretability and providing insight into the kinematic dependence of angular observables.
Source: Journal of High Energy Physics
Institutions: European Organization for Nuclear Research, Consejo Superior de Investigaciones Científicas, Universitat de València, Scuola Internazionale Superiore di Studi Avanzati, Istituto Nazionale di Fisica Nucleare, Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, University of Cambridge
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
Awards: G103507813, G1105844351, G1175115578, G1272900390, G1610934229, G1971932983, G2262748287, G2601397997, G300979063, Particles, Fields and Extended Objects, G3452655474, G3480869486, G3480961101, G3681454997, G4014979219, G4042783231, G425668148, G434337789, G4511208782, G464105488, G4679674362, Quantum Fields, Quantum Gravity and Quantum Particles, G491712853, G5080475149, G5195188938, G5209569390, G5222023348, G5619011875, G5635532810, G5655158037, G7084143925, G7502457081, G7805626310, G7863371703, G8497050386
OpenAlex ID: W7128161902