J. Rojo

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 6 and 8 are both negative.

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.

Unbiased determination of the proton structure function F2p with faithful uncertainty estimation

We construct a parametrization of the deep-inelastic structure function of the proton F2(x,Q2) 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.

Borel resummation of soft gluon radiation and higher twists

Abstract 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.

Heavy ions at the Future Circular Collider

The Future Circular Collider (FCC) Study is aimed at assessing the physics potential and the technical feasibility of a new collider with centre-of-mass energies, in the hadron-hadron collision mode, seven times larger than the nominal LHC energies. Operating such machine with heavy ions is an option that is being considered in the accelerator design studies. It would provide, for example, Pb-Pb and p-Pb collisions at sqrt{s_NN} = 39 and 63 TeV, respectively, per nucleon-nucleon collision, with integrated luminosities above 30 nb^-1 per month for Pb-Pb. This is a report by the working group on heavy-ion physics of the FCC Study. First ideas on the physics opportunities with heavy ions at the FCC are presented, covering the physics of the Quark-Gluon Plasma, of gluon saturation, of photon-induced collisions, as well as connections with other fields of high-energy physics.

Nonperturbative states in type II superstring theory from classical spinning membranes

Abstract 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 nonperturbative 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 × S 4 and AdS 4 × 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.

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 mb1S in a way that theoretical uncertainties are minimized, for which the value mb1S = 4.84±0.14exp±0.05th GeV is obtained.

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

We study QCD in 1+1 dimensions in the large Nc limit using light-front Hamiltonian perturbation theory in the 1/Nc expansion. We use this formalism to exactly compute hadronic transition matrix elements for arbitrary currents at leading order in 1/Nc. We compute the semileptonic differential decay rate of a heavy meson, dΓ/dx, and its moments, MN, 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 ~ 1−x ~ 1) or non-local effective field theories (1/N ~ 1−x ~ ΛQCD/mQ) 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(ΛQCD2/mQ2) in the kinematic region where the operator product expansion can be applied, and with relative accuracy of O(ΛQCD/mQ) 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.

Neural network approach to parton distributions fitting

We will show an application of neural networks to extract informations 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.

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.