arXiv subjects

Andrea Piccione

Publications and source records attributed to Andrea Piccione.

16 recordsLinked to original sources

arXiv2025

QED effects in inclusive semi-leptonic $B$ decays

We analyse in detail the QED corrections to the total decay width and the moments of the electron energy spectrum of the inclusive semi-leptonic $B \to X_c e ν$ decay. Our calculation includes short-distance electroweak corrections, the complete ${\cal O}(α)$ partonic terms and leading-logarithmic QED effects up to ${\cal O}(Λ^3_{\rm QCD}/m_b^3)$. A comprehensive numerical comparison of our results against those obtained with the Monte Carlo (MC) tool PHOTOS is presented. While the comparison indicates good overall agreement, our computation contains QED effects not included in PHOTOS and should therefore better describe photon radiation to $B \to X_c e ν$ as measured by the $B$-factories. Our calculations represent the first steps in the construction of a fully differential higher-order QED MC generator for inclusive semi-leptonic $B$ decays.

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arXiv2009

Precision determination of electroweak parameters and the strange content of the proton from neutrino deep-inelastic scattering

We use recent neutrino dimuon production data combined with a global deep-inelastic parton fit to construct a new parton set, NNPDF1.2, which includes a determination of the strange and antistrange distributions of the nucleon. The result is characterized by a faithful estimation of uncertainties thanks to the use of the NNPDF methodology, and is free of model or theoretical assumptions other than the use of NLO perturbative QCD and exact sum rules. Better control of the uncertainties of the strange and antistrange parton distributions allows us to reassess the determination of electroweak parameters from the NuTeV dimuon data. We perform a direct determination of the |V_cd| and |V_cs| CKM matrix elements, obtaining central values in agreement with the current global CKM fit: specifically we find |V_cd|=0.244\pm 0.019 and |V_cs|=0.96\pm 0.07. Our result for |V_cs| is more precise than any previous direct determination. We also reassess the uncertainty on the NuTeV determination of \sin^2θ_W through the Paschos-Wolfenstein relation: we find that the very large uncertainties in the strange valence momentum fraction are sufficient to bring the NuTeV result into complete agreement with the results from precision electroweak data.

hep-ph↗

arXiv2009

The Bjorken sum rule with Monte Carlo and Neural Network techniques

Determinations of structure functions and parton distribution functions have been recently obtained using Monte Carlo methods and neural networks as universal, unbiased interpolants for the unknown functional dependence. In this work the same methods are applied to obtain a parametrization of polarized Deep Inelastic Scattering (DIS) structure functions. The Monte Carlo approach provides a bias--free determination of the probability measure in the space of structure functions, while retaining all the information on experimental errors and correlations. In particular the error on the data is propagated into an error on the structure functions that has a clear statistical meaning. We present the application of this method to the parametrization from polarized DIS data of the photon asymmetries $A_1^p$ and $A_1^d$ from which we determine the structure functions $g_1^p(x,Q^2)$ and $g_1^d(x,Q^2)$, and discuss the possibility to extract physical parameters from these parametrizations. This work can be used as a starting point for the determination of polarized parton distributions.

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arXiv2009

A determination of parton distributions with faithful uncertainty estimation

We present the determination of a set of parton distributions of the nucleon, at next-to-leading order, from a global set of deep-inelastic scattering data: NNPDF1.0. The determination is based on a Monte Carlo approach, with neural networks used as unbiased interpolants. This method, previously discussed by us and applied to a determination of the nonsinglet quark distribution, is designed to provide a faithful and statistically sound representation of the uncertainty on parton distributions. We discuss our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the evolution equations and its benchmarking, and the method used to compute physical observables. We discuss the parametrization and fitting of neural networks, and the algorithm used to determine the optimal fit. We finally present our set of parton distributions. We discuss its statistical properties, test for its stability upon various modifications of the fitting procedure, and compare it to other recent parton sets. We use it to compute the benchmark W and Z cross sections at the LHC. We discuss issues of delivery and interfacing to commonly used packages such as LHAPDF.

hep-ph↗

arXiv2008

Update on Neural Network Parton Distributions: NNPDF1.1

We present recent progress within the NNPDF parton analysis framework. After a brief review of the results from the DIS NNPDF analysis, NNPDF1.0, we discuss results from an updated analysis with independent parametrizations for the strange and anti-strange distributions, denoted by NNPDF1.1. We examine the phenomenological implications of this improved analysis for the strange PDFs.

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arXiv2007

Neural network determination of parton distributions: the nonsinglet case

We provide a determination of the isotriplet quark distribution from available deep--inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next--to--next--to--leading order, and compare them with those obtained using other approaches.

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arXiv2006

Neural network determination of the non-singlet quark distribution

We summarize the main features of our approach to parton fitting, and we show a preliminary result for the non-singlet structure function. When comparing our result to other PDF sets, we find a better description of large x data and larger error bands in the extrapolation regions.

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arXiv2005

Unbiased determination of the proton structure function F_2^p with faithful uncertainty estimation

We construct a parametrization of the deep-inelastic structure function of the proton F_2 based on all available experimental information from charged lepton deep-inelastic scattering experiments. The parametrization effectively provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. The result is obtained in the form of a Monte Carlo sample of neural networks trained on an ensemble of replicas of the experimental data. We discuss in detail the techniques required for the construction of bias-free parameterizations of large amounts of structure function data, in view of future applications to the determination of parton distributions based on the same method.

hep-ph↗

arXiv2005

Neural network approach to parton distributions fitting

We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.

hep-ph↗

arXiv2005

The neural network approach to parton distributions

We introduce the neural network approach to global fits of parton distrubution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.

hep-ph↗

arXiv2005

The neural network approach to parton fitting

We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.

hep-ph↗

arXiv2002

Neural Network Parametrization of Deep-Inelastic Structure Functions

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F_2 of the proton and deuteron, and a precision determination of the isotriplet combination F_2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.

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arXiv2002

Aspects of QCD perturbative evolution

This thesis is devoted to the study of some aspects of perturbative QCD, and in particular to the development of high-precision techniques for the extraction of physical parameters such as structure functions, parton distributions, and the strong coupling from the analysis of deep inelastic scattering data. First, we will discuss scaling violations of singlet and nonsinglet truncated moments, and the use of truncated momets to solve the Altarelli-Parisi equation. Then we will suggest an approach based on neural networks to the parametrization and interpolation of experimental data, which retains information on experimental errors and correlations. The method of truncated moments can be combined with the neural network fit to extract various quantities of phenomenological interest in a bias-free way. As an example of such application, we will discuss the determination of the strong coupling constant.

hep-ph↗

arXiv2002

Determination of alpha_s from scaling violations of truncated moments of structure functions

We determine the strong coupling alpha_s(M_Z) from scaling violations of truncated moments of the nonsinglet deep inelastic structure function F_2. Truncated moments are determined from BCDMS and NMC data using a neural network parametrization which retains the full experimental information on errors and correlations. Our method minimizes all sources of theoretical uncertainty and bias which characterize extractions of alpha_s from scaling violations. We obtain alpha_s(M_Z) = 0.124 +0.004-0.007 (exp.) + 0.003- 0.004 (th.).

hep-ph↗

arXiv2001

Solving the Altarelli-Parisi equations with truncated moments

The technique of truncated moments of parton distributions allows us to study scaling violations without making any assumption on the shape of parton distributions. The numerical implementation of the method is however difficult, since the evolution equations for truncated moments are not diagonal. We present a simple way to improve the efficiency of the numerical solution of the evolution equations for truncated moments. As a result, the number of truncated moments needed to achieve the required precision in the evolution is significantly smaller than in the original formulation of the technique. The method presented here can also be used to obtain the value of parton distributions in terms of truncated moments, and therefore it can be viewed as a technique for the solution of the Altarelli-Parisi equations.

hep-ph↗

arXiv2000

Evolution of truncated moments of singlet parton distributions

We define truncated Mellin moments of parton distributions by restricting the integration range over the Bjorken variable to the experimentally accessible subset x_0 < x < 1 of the allowed kinematic range 0 < x < 1. We derive the evolution equations satisfied by truncated moments in the general (singlet) case in terms of an infinite triangular matrix of anomalous dimensions which couple each truncated moment to all higher moments with orders differing by integers. We show that the evolution of any moment can be determined to arbitrarily good accuracy by truncating the system of coupled moments to a sufficiently large but finite size, and show how the equations can be solved in a way suitable for numerical applications. We discuss in detail the accuracy of the method in view of applications to precision phenomenology.

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