arXiv subjects

Amedeo Chiefa

Publications and source records attributed to Amedeo Chiefa.

6 recordsLinked to original sources

arXiv2026

First next-to-next-to-leading-order extraction of fragmentation functions for Lambda hyperons

We present MAPFF1.0_Lambda, the first global analysis at next-to-next-to-leading order in perturbative QCD of the collinear unpolarised fragmentation functions of Lambda hyperons. The fit is based on data from single-inclusive electron-positron annihilation, and from both neutral-current and -- for the first time -- charged-current semi-inclusive deep-inelastic scattering. We have adopted a statistical framework based on Monte Carlo sampling and parametrised fragmentation functions in terms of a neural network. The fragmentation function set comprises a total of seven independent parton flavours, allowing for the first independent determination of valence-quark distributions. Our analysis offers new insights into the hadronisation mechanism of strange baryons and establishes a baseline for future phenomenological and experimental investigations.

hep-ph↗

arXiv2025

Quantitative Understanding of PDF Fits and their Uncertainties

Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons. As the LHC enters an era of high-precision measurements, a robust PDF determination with a reliable uncertainty quantification has become mandatory in order to match the experimental precision. The NNPDF collaboration has pioneered the use of Machine Learning (ML) techniques for PDF determinations, using Neural Networks (NNs) to parametrise the unknown PDFs in a flexible and unbiased way. The NNs are then trained on experimental data by means of stochastic gradient descent algorithms. The statistical robustness of the results is validated by extensive closure tests using synthetic data. In this work, we develop a theoretical framework based on the Neural Tangent Kernel (NTK) to analyse the training dynamics of neural networks. This approach allows us to derive, under precise assumptions, an analytical description of the neural network evolution during training, enabling a quantitative understanding of the training process. Having an analytical handle on the training dynamics allows us to clarify the role of the NN architecture and the impact of the experimental data in a transparent way. Similarly, we are able to describe the evolution of the covariance of the NN output during training, providing a quantitative description of how uncertainties are propagated from the data to the fitted function. While our results are not a substitute for PDF fitting, they do provide a powerful diagnostic tool to assess the robustness of current fitting methodologies. Beyond its relevance for particle physics phenomenology, our analysis of PDF determinations provides a testbed to apply theoretical ideas about the learning process developed in the ML community.

hep-ph↗

arXiv2025

Parton distributions with higher twist and jet power corrections

We present a global determination of parton distribution functions (PDFs) that accounts for higher twist corrections in deep-inelastic scattering (DIS) and linear power corrections for single inclusive jet and dijet production data from the LHC. We determine these corrections and their associated correlated uncertainties using a methodology based on the theory covariance formalism, previously used to account for nuclear uncertainties and missing higher order uncertainties (MHOUs) in global PDF determinations. We then study the impact of the power corrections on the extracted PDFs, and demonstrate an improved description of the data due to a reduced sensitivity to DIS data in the low-$x$ region where higher twist uncertainties are relatively large, and a reduced sensitivity to single inclusive jet data at relatively low $p_T$, where linear power corrections can be significant. Finally, we assess the impact of power corrections on observables relevant to LHC phenomenology, including Higgs production via gluon fusion, and the determination of $\alpha_s$. We find that these effects, while small, can be significant, improving perturbative convergence.

hep-ph↗

arXiv2025

Parton distributions confront LHC Run II data: a quantitative appraisal

We present a systematic comparison of theoretical predictions and various high-precision experimental measurements, specifically of differential cross sections performed by the LHC run II for Drell-Yan gauge boson, top-quark pair, single-inclusive jet and di-jet production, and by HERA for single-inclusive jet and di-jet production. Theoretical predictions are computed at next-to-next-to-leading order (NNLO) accuracy in perturbative Quantum Chromodynamics. The most widely employed sets of Parton Distribution Functions (PDFs) are used, and PDF, strong coupling, and missing higher order uncertainties are taken into account. We quantitatively assess the predictive power of each PDF set and the contribution of the different sources of experimental and theoretical uncertainty to the agreement between data and predictions. We show that control over all of these aspects is crucial to precision physics studies, such as the determination of Standard Model parameters at the LHC.

hep-ph↗

arXiv2024

Status and Developments in Polarised Parton Distribution Functions

The need for accurate and precise polarised parton distribution functions (PDFs) is becoming increasingly crucial in view of the Electron-Ion Collider experimental program foreseen in the coming years. Two global PDF determinations at next-to-next-to-leading order accuracy have been recently presented, MAPPDFpol1.0 and BDSSV24. I review the former and I provide a comparative discussion of other PDF sets accurate to next-to-leading order. I show that differences between these PDF sets, due to the choice of experimental and methodological input, exceed differences due to perturbative accuracy.

hep-ph↗

arXiv2024

Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data

We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination includes, for the first time, next-to-next-to-leading order QCD corrections to both processes, and is carried out in a framework that combines a neural-network parametrisation of PDFs with a Monte Carlo representation of their uncertainties. We discuss the quality of the determination, in particular its dependence on higher-order corrections, on the choice of data set, and on theoretical constraints.

hep-ph↗