arXiv subjects

Joan Rojo

Publications and source records attributed to Joan Rojo.

14 recordsLinked to original sources

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.

hep-ph↗

arXiv2006

Heavy meson semileptonic differential decay rate in two dimensions in the large Nc

We study QCD in 1+1 dimensions in the large $N_c$ limit using light-front Hamiltonian perturbation theory in the $1/N_c$ expansion. We use this formalism to exactly compute hadronic transition matrix elements for arbitrary currents at leading order in $1/N_c$. We compute the semileptonic differential decay rate of a heavy meson, $dΓ/dx$, and its moments, $M_N$, using the hadronic matrix elements obtained previously. We put some emphasis in trying to understand parity invariance. We also study with special care the kinematic region where the operator product expansion ($1/N \sim 1-x \sim 1$) or non-local effective field theories ($1/N \sim 1-x \sim Λ_{QCD}/m_Q$) can be applied. We then compare with the results obtained using an effective field theory approach based on perturbative factorization, with the focus to better understand quark-hadron duality. At the end of the day, using effective field theories, we have been able to obtain expressions for the moments with relative accuracy of $O(Λ_{QCD}^2/m_Q^2)$ in the kinematic region where the operator product expansion can be applied, and with relative accuracy of $O(Λ_{QCD}/m_Q)$ in the kinematic region where non-local effective field theories can be applied. These expressions agree, within this precision, with those obtained from the hadronic result using the layer-function approximation plus Euler-McLaurin expansion. Very good numerical agreement for the moments is obtained between the exact result and the result using effective field theories.

hep-ph↗

arXiv2006

Determination of the atmospheric neutrino fluxes from experimental data

The precise knowledge of the atmospheric neutrino fluxes is a key ingredient in the interpretation of the results from any atmospheric neutrino experiment. In the standard atmospheric neutrino data analysis, these fluxes are theoretical inputs obtained from sophisticated numerical calculations. In this contribution we present an alternative approach to the determination of the atmospheric neutrino fluxes based on the direct extraction from the experimental data on neutrino event rates.

astro-ph↗

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.

hep-ph↗

arXiv2006

The neural network approach to parton distribution functions

We introduce the neural network approach to the parametrization of parton distributions. After a general introduction, we present in detail our approach to parametrize experimental data, based on a combination of Monte Carlo methods and neural networks. We apply this strategy first in three different cases: the proton structure function, hadronic tau decays and B meson decay spectra. Finally we describe the neural network approach applied to the parametrization of parton distribution functions, and present results on the nonsinglet parton distribution.

hep-ph↗

arXiv2006

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.

hep-ph↗

arXiv2006

Borel resummation of soft gluon radiation and higher twists

We show that the well-known divergence of the perturbative expansion of resummed results for processes such as deep-inelastic scattering and Drell-Yan in the soft limit can be treated by Borel resummation. The divergence in the Borel inversion can be removed by the inclusion of suitable higher twist terms. This provides us with an alternative to the standard 'minimal prescription' for the asymptotic summation of the perturbative expansion, and it gives us some handle on the role of higher twist corrections in the soft resummation region.

hep-ph↗

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↗

arXiv2004

Non-perturbative states in type II superstring theory from classical spinning membranes

We find a new family of exact solutions in membrane theory, representing toroidal membranes spinning in several planes. They have energy square proportional to the sum of the different angular momenta, generalizing Regge-type string solutions to membrane theory. By compactifying the eleven dimensional theory on a circle and on a torus, we identify a family of new non-perturbative states of type IIA and type IIB superstring theory (which contains the perturbative spinning string solutions of type II string theory as a particular case). The solution represents a spinning bound state of D branes and fundamental strings. Then we find similar solutions for membranes on $AdS_7\times S^4$ and $AdS_4\times S^7$. We also consider the analogous solutions in SU(N) matrix theory, and compute the energy. They can be interpreted as rotating open strings with D0 branes attached to their endpoints.

hep-th↗

arXiv2004

A probability measure in the space of spectral functions and structure functions

We present a novel technique to parametrize experimental data, based on the construction of a probability measure in the space of functions, which retains the full experimental information on errors and correlations. This measure is constructed in a two step process: first, a Monte Carlo sample of replicas of the experimental data is generated, and then an ensemble of neural network is trained over them. This parametrization does not introduce any bias due to the choice of a fixed functional form. Two applications of this technique are presented. First a probability measure in the space of the spectral function $ρ_{V-A}(s)$ is generated, which incorporates theoretical constraints as chiral sum rules, and is used to evaluate the vacuum condensates. Then we construct a probability measure in the space of the proton structure function $F_2^p(x,Q^2)$, which updates previous work, incorporating HERA data.

hep-ph↗

arXiv2004

Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates

The spectral function $ρ_{V-A}(s)$ is determined from ALEPH and OPAL data on hadronic tau decays using a neural network parametrization trained to retain the full experimental information on errors, their correlations and chiral sum rules: the DMO sum rule, the first and second Weinberg sum rules and the electromagnetic mass splitting of the pion sum rule. Nonperturbative QCD vacuum condensates can then be determined from finite energy sum rules. Our method minimizes all sources of theoretical uncertainty and bias producing an estimate of the condensates which is independent of the specific finite energy sum rule used. The results for the central values of the condensates $O_6$ and $O_8$ are both negative.

hep-ph↗