arXiv subjects

Giovanni De Crescenzo

Publications and source records attributed to Giovanni De Crescenzo.

3 recordsLinked to original sources

arXiv2026

MadNIS at NLO

We combine fast amplitude surrogates with neural importance sampling to accelerate NLO calculations. For virtual corrections, a learned ratio to the Born matrix element with calibrated uncertainties guarantees reliable precision across phase space. For real emission, we stick to the standard FKS subtraction and train sector-conditioned surrogates of the regularized integrands away from divergences. MadNIS then uses multi-channel mappings and FKS sectors as conditions. We validate our approach for electron-positron scattering to three and four jets and find significant speed-ups and variance reduction in the integration.

hep-ph↗

arXiv2025

Advancing Tools for Simulation-Based Inference

We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and derivative learning. Technically, we introduce a new and more efficient smearing algorithm, illustrate how uncertainties can be approximated through repulsive ensembles, and show how equivariant networks can improve likelihood estimation. After illustrating these aspects for a toy model, we target di-boson production at the LHC and find that our improvements significantly increase numerical control and stability.

hep-ph↗

arXiv2025

Evaluating the faithfulness of PDF uncertainties in the presence of inconsistent data

We critically assess the robustness of uncertainties on parton distribution functions (PDFs) determined using neural networks from global sets of experimental data collected from multiple experiments. We view the determination of PDFs as an inverse problem, and we study the way the neural network model tackles it when inconsistencies between input datasets are present. We use a closure test approach, in which the regression model is applied to artificial data produced from a known underlying truth, to which the output of the model can be compared and its accuracy can be assessed in a statistically reliable way. We explore various phenomenologically relevant scenarios in which inconsistencies arise due to incorrect estimation of correlated systematic uncertainties. We show that the neural network generally corrects for the inconsistency except in cases of extreme uncertainty underestimation. When the inconsistency is not corrected, we propose and validate a procedure to detect inconsistencies.

hep-ph↗