arXiv subjects

Manuel Morales-Alvarado

Publications and source records attributed to Manuel Morales-Alvarado.

7 recordsLinked to original sources

arXiv2026

PDF effects in high-mass Drell-Yan SMEFT analyses across flavour space

High-mass Drell-Yan dilepton production provides one of the most sensitive probes of semileptonic four-fermion operators in the Standard Model Effective Field Theory (SMEFT), thanks to the energy growth of the corresponding contributions. At the same time, these measurements also constrain parton distribution functions (PDFs) in the large-$x$ region, where PDF uncertainties can mimic or obscure smooth new-physics effects. In this work we study how the impact of PDF profiling on SMEFT constraints depends on the quark-flavour structure of the effective operators, by performing a joint fit of SMEFT Wilson coefficients and PDF nuisance parameters. We find that PDF profiling induces a strongly flavour-dependent degradation of the SMEFT sensitivity, both with current data and in HL-LHC projections. The largest broadenings occur for operators involving first-generation quarks, whose effects are correlated with the high-$x$ valence-quark luminosities that dominate the high-mass spectrum. Operators involving heavier quark flavours are less affected, although with non-negligible operator-dependent variations. As a byproduct of our analysis, we obtain an estimate on the changes induced in the PDF sector by profiling SMEFT effects. We also show that angular information provides an important handle to reduce degeneracies among SMEFT directions and between SMEFT effects and allowed PDF deformations. These results demonstrate that the relevance of PDF uncertainties in high-energy SMEFT fits is not uniform across flavour space, and must be assessed in a flavour-dependent way.

hep-ph↗

arXiv2026

Tailored PDFs for New Physics searches

Given the non-negligible interplay between parton distribution functions (PDFs) at large x and potential New Physics (NP) effects in the high-energy tails of hadron collider observables, a central question is which PDFs can be reliably employed in beyond-the-Standard-Model (BSM) analyses. In this work, we examine the fine balance between using PDF sets with small uncertainties in the large-x region -- crucial for maximising BSM sensitivity -- and adopting conservative PDF fits that exclude high-energy data potentially contaminated by unaccounted NP contributions. We systematically assess a range of conservative PDF fitting strategies designed to mitigate such biases and provide a recommendation for the class of PDFs best suited for robust BSM searches. In addition, we investigate the alternative approach of performing simultaneous fits of Standard Model Effective Field Theory (SMEFT) Wilson coefficients and PDFs, thereby consistently accounting for their mutual correlations. Starting from a toy model to illustrate the underlying mechanisms, we then analyse two realistic NP scenarios: one modifying high-mass Drell-Yan production and another affecting the high-invariant-mass tail of top-quark pair production. Both cases are representative of measurements that will be probed with high precision during the High-Luminosity phase of the LHC.

hep-ph↗

arXiv2025

Angular Coefficients from Interpretable Machine Learning with Symbolic Regression

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 $pp \to \ell^+\ell^-$ 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.

hep-ph↗

arXiv2025

Quark mixing from muon collider neutrinos

A high energy muon collider naturally produces a collimated beam of neutrinos for a fixed-target experiment at a dedicated far-forward facility. The high intensity and energy of the beam makes it ideally suited for astonishingly precise measurements of neutrino scattering on nucleons in the deeply inelastic regime, enabling the determination of the Cabibbo--Kobayashi--Maskawa~(CKM) quark mixing matrix. We assess the floor to the attainable sensitivity set by irreducible sources of uncertainties from the imperfect knowledge of the parton distribution (PDF) and fragmentation functions, showing that a strong improvement is possible well above current standards. As a by-product, our analysis also outlines extraordinary perspectives for a combined determination of the PDF. The results demonstrate the potential of a parasitic neutrino experiment at the muon collider, motivating detailed future studies.

hep-ph↗

arXiv2025

Foundation models for equation discovery in high energy physics

Foundation models, large machine learning models trained on broad, multimodal datasets, have been gaining increasing attention in scientific applications due to their strong performance on diverse downstream tasks. Large Language Models (LLMs), a prominent instance of foundation models, have achieved remarkable success in tasks such as text and image generation. In this work, we investigate their potential for equation discovery in high energy physics, focusing on symbolic regression. We apply the LLM-SR methodology both to benchmark problems of equation recovery in lepton angular distributions and to the discovery of functional forms for angular coefficients in electroweak boson production at the Large Hadron Collider, observables of high phenomenological relevance for which no closed-form expressions are known from first principles. Our results demonstrate that LLM-SR can uncover compact, accurate, and interpretable equations across in-domain and out-of-domain kinematic regions, effectively incorporating embedded scientific knowledge and offering a promising new approach to equation discovery in high energy physics.

hep-ph↗

arXiv2025

Statistical Analysis of Scientific Metrics in High Energy, Cosmology, and Astroparticle Physics in Latin America

We perform a comprehensive statistical analysis of key scientific metrics to evaluate the productivity and impact of research conducted in Latin American countries within the fields of High Energy Physics, Cosmology and Astroparticle Physics (HECAP). Using data from the widely used open-access digital library INSPIRE-HEP, we provide a detailed assessment of the scientific contributions from the continent over the past 70 years. We provide data for the evolution of the overall productivity in the region relative to the rest of the world, comparing the productivity of each country, number of active researchers, number of publications, citations, h-index in total and relative to the population and number of researchers, as well as the productivity and impact compared to the percentage of Gross Domestic Product (GDP) invested in research, and the Human Development Index (HDI) of each country. We also analyse collaborations among the different countries, as well as collaborations with the rest of the world. Additionally, we studied the gender gap evolution over the same period. This pioneering analysis, which relies solely on open data, can serve as an essential resource for researchers and policymakers alike. It aims to empower scientists with insights into the significance of their contributions to both regional and global research. Moreover, it provides both researchers and policymakers with critical quantitative data, strengthening their understanding of the progress in scientific productivity over the years to better support scientific endeavours in Latin America.

physics.soc-ph↗

arXiv2024

Symbolic regression for precision LHC physics

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 the Large Hadron Collider (LHC). As a benchmark, we apply SR to equation recovery in quantum electrodynamics (QED), where established analytical results from quantum field theory provide a reliable framework for evaluation. This benchmark serves to validate the performance and reliability of SR before extending its application to structure functions in the Drell-Yan process mediated by virtual photons, which lack analytic representations from first principles. By combining the simplicity of analytic expressions with the predictive power of machine learning techniques, SR offers a useful tool for facilitating phenomenological analyses in high energy physics.

hep-ph↗