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.Comment: 24 pages, 6 figure

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.Comment: 46 pages, 18 figures, LaTeX with JHEP3 clas

Neural network parametrization of the lepton energy spectrum in semileptonic B meson decays

We construct a parametrization of the lepton energy spectrum in inclusive semileptonic decays of B mesons, based on the available experimental information: moments of the spectrum with cuts, their errors and their correlations, together with kinematical constraints. The result is obtained in the form of a Monte Carlo sample of neural networks trained on replicas of the experimental data, which represents the probability density in the space of lepton energy spectra. This parametrization is then used to extract the b quark mass m_b^{1S} in a way that theoretical uncertainties are minimized, for which the value m_b^{1S}=4.84 \pm 0.14^{exp}\pm 0.05^{th} GeV is obtained.Comment: 32 pages, 22 figures, JHEP3 class. v4 version accepted for publication in JHE

Polarized Parton Distributions at an Electron-Ion Collider

We study the potential impact of inclusive deep-inelastic scattering data from a future electron-ion collider (EIC) on longitudinally polarized parton distribution (PDFs). We perform a PDF determination using the NNPDF methodology, based on sets of deep-inelastic EIC pseudodata, for different realistic choices of the electron and proton beam energies. We compare the results to our current polarized PDF set, NNPDFpol1.0, based on a fit to fixed-target inclusive DIS data. We show that the uncertainties on the first moments of the polarized quark singlet and gluon distributions are substantially reduced in comparison to NNPDFpol1.0, but also that more measurements may be needed to ultimately pin down the size of the gluon contribution to the nucleon spin.Comment: 11 pages, 6 figures. Two plots in Fig.5 added and discussion of extrapolation uncertainties expanded. Final version, published in Phys. Lett.

Recent progress on NNPDF for LHC

We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution Functions (PDFs). Our method is based on the idea of combining a Monte Carlo sampling of the probability measure in the space of PDFs with the use of neural networks as unbiased universal interpolating functions. The general structure of the project and the features of the fit are described and compared to those of the traditional approaches.Comment: 4 pages, 6 figures, contribution for the proceedings of the conference "Rencontres de Moriond, QCD and High Energy Interactions

Progress on neural parton distributions

We give a status report on the determination of a set of parton distributions based on neural networks. In particular, we summarize the determination of the nonsinglet quark distribution up to NNLO, we compare it with results obtained using other approaches, and we discuss its use for a determination of $\alpha_s$.Comment: 4 pages, 2 figs, uses dis2007.cls, to appear in the DIS 2007 workshop proceeding

Parton distributions: determining probabilities in a space of functions

We discuss the statistical properties of parton distributions within the framework of the NNPDF methodology. We present various tests of statistical consistency, in particular that the distribution of results does not depend on the underlying parametrization and that it behaves according to Bayes' theorem upon the addition of new data. We then study the dependence of results on consistent or inconsistent datasets and present tools to assess the consistency of new data. Finally we estimate the relative size of the PDF uncertainty due to data uncertainties, and that due to the need to infer a functional form from a finite set of data.Comment: 11 pages, 8 figures, presented by Stefano Forte at PHYSTAT 2011 (to be published in the proceedings

EPS09 - a New Generation of NLO and LO Nuclear Parton Distribution Functions

We present a next-to-leading order (NLO) global DGLAP analysis of nuclear parton distribution functions (nPDFs) and their uncertainties. Carrying out an NLO nPDF analysis for the first time with three different types of experimental input -- deep inelastic $\ell$+A scattering, Drell-Yan dilepton production in p+$A$ collisions, and inclusive pion production in d+Au and p+p collisions at RHIC -- we find that these data can well be described in a conventional collinear factorization framework. Although the pion production has not been traditionally included in the global analyses, we find that the shape of the nuclear modification factor $R_{\rm dAu}$ of the pion $p_T$-spectrum at midrapidity retains sensitivity to the gluon distributions, providing evidence for shadowing and EMC-effect in the nuclear gluons. We use the Hessian method to quantify the nPDF uncertainties which originate from the uncertainties in the data. In this method the sensitivity of $\chi^2$ to the variations of the fitting parameters is mapped out to orthogonal error sets which provide a user-friendly way to calculate how the nPDF uncertainties propagate to any factorizable nuclear cross-section. The obtained NLO and LO nPDFs and the corresponding error sets are collected in our new release called {\ttfamily EPS09}. These results should find applications in precision analyses of the signatures and properties of QCD matter at the LHC and RHIC.Comment: 34 pages, 16 figures. The version accepted for publicatio

PENGARUH PENGGUNAAN METODE PEMBELAJARAN KOOPERATIF MAKE A MATCH TERHADAP

bstract: The purpose of this research is to looking for the influence of the using Cooperative learning type make a match to the student’s achievment. This research uses experiment method. The population is all students of 5 grade elementary school Wonogiri Wonogiri. The sample was selected using stratified cluster random sampling. The data resources focused in the achievement learning that gotten by pretest and posttest using questions test that had trough the research of validity, reability, difficulty index and ability of differentiation of question test. The pre-analytic of data are test of balance, normality test, and homogenity test. The technique of analytic of data that used to hipotesis test is t test. The result of the research can be concluded that there is a positif influence of using cooperative learning type make a match to the student’s achievement (t calculate > t table=56,691>2,00). Abstrak: Tujuan penelitian ini adalah untuk mengetahui pengaruh metode pembelajaran kooperatif make a match terhadap hasil belajar IPS. Penelitian ini menggunakan metode eksperimen. Populasi penelitian ini adalah siswa kelas 5 SDN Wonogiri, Wonogiri. Teknik pengambilan sampel yang digunakan adalah stratified cluster random sampling. Sumber data difokuskan pada proses belajar dan hasil belajar yang diperoleh melalui pretest dan posttest dengan instrument soal yang telah melalui uji validitas, reabilitas, indeks kesukaran dan daya pembeda soal. Uji prasyarat analisis menggunakan uji keseimbangan, uji normalitas, uji homogenitas. Sedangkan teknik analisis data sebagai uji hipotesis memakai uji t. Hasil dari penelitian ini dapat disimpulkan ada pengaruh yang positif signifikan model pembelajaran kooperatif Make a Match terhadap hasil belajar IPS (t hitung > t tabel =56,691>2,00). Kata Kunci: Pembelajaran Kooperatif, Make a Matc

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.Comment: 5 pages, 6 figures, proceedings of the International Symposium on Multiparticle Dynamics 08, 15-20 september 2008, DES