Add to ORKGWe present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly useful for an anaysis directed at extracting generalized parton distributions from data. We show quantitative results of our initial analysis of the parton distribution functions from inclusive deep inelastic scattering. 1
Self-organizing maps (SOM) are among the more popular neural network models first studied by Teuvo K...
We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution F...
We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution F...
We present an alternative algorithm to global fitting procedures to construct Parton Distribution Fu...
We introduce the neural network approach to the parametrization of parton distributions. After a gen...
We will show an application of neural networks to extract informations on the structure of hadrons. ...
We provide a determination of the isotriplet quark distribution from available deep-inelastic data u...
We provide a determination of the isotriplet quark distribution from available deep--inelastic data ...
We present the determination of a set of parton distributions of the nucleon, at next-to-leading ord...
In this contribution, we present a status report on the recent progress towards an analysis of nucle...
I review recent progress in the determination of the parton structure of the nucleon, in particular ...
International audienceWe extract two nonsinglet nucleon Parton Distribution Functions from lattice Q...
We present a determination of the parton distributions of the nucleon from a global set of hard scat...
Since the first determination of a structure function many decades ago, all methodologies used to de...
We give a status report on the determination of a set of parton distributions based on neural networ...
Self-organizing maps (SOM) are among the more popular neural network models first studied by Teuvo K...
We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution F...
We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution F...
We present an alternative algorithm to global fitting procedures to construct Parton Distribution Fu...
We introduce the neural network approach to the parametrization of parton distributions. After a gen...
We will show an application of neural networks to extract informations on the structure of hadrons. ...
We provide a determination of the isotriplet quark distribution from available deep-inelastic data u...
We provide a determination of the isotriplet quark distribution from available deep--inelastic data ...
We present the determination of a set of parton distributions of the nucleon, at next-to-leading ord...
In this contribution, we present a status report on the recent progress towards an analysis of nucle...
I review recent progress in the determination of the parton structure of the nucleon, in particular ...
International audienceWe extract two nonsinglet nucleon Parton Distribution Functions from lattice Q...
We present a determination of the parton distributions of the nucleon from a global set of hard scat...
Since the first determination of a structure function many decades ago, all methodologies used to de...
We give a status report on the determination of a set of parton distributions based on neural networ...
Self-organizing maps (SOM) are among the more popular neural network models first studied by Teuvo K...
We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution F...
We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution F...