| topics |
| monte carlo and neural networks |
We developed a technique to perform unbiased fits of parametrizations to experimental data.
We used a Neural Network parametrization to minimize the impact of theoretical assumtpions
on the fitted function. We used a Monte Carlo sample to propagate errors and correlations
from experimental data to the parametrization and from the parametrization to any observable which can be evaluated with it.
This technique was presented in [hep-ph/0204232]
and then futher improved by the NNPDF collaboration.
|
| parton distributions functions |
Parton distribution functions (PDFs) are an essential ingredient for the LHC, and the high-precision
title from HERA have opened the possibility for a precise determination of parton distributions. This
requires an improvement in the theoretical description of deep inelastic scattering (DIS) and hard hadronic scattering processes,
as well as an improvement of the techniques used to extract parton distributions from experimental data.
For this purpose, we are implementing the Monte Carlo - Neural Networks technique in the PDFs case.
Results for a complete set of PDFs extracted from DIS data are published as
NNPDF1.0. We are working
the implementation of hadronic observables.
|
| spin physics |
We use the Monte Carlo - Neural Networks technique to fit the virtual photon asymmetry A1
of polarized DIS. The results of the fit are the inputs to evaluate the Bjorken sum rule and to
extract the strong coupling αs.
|
| talks |
| march 2015 |
Webinar Pearson Academy: Storytelling - adesso vi racconto una storia slide e video |
| october 2014 |
Webinar Pearson Academy: Come sopravvivere in classi difficili e riuscire a insegnare qualcosa slide e video |
| september 2008 |
NNPDF1.0: benchmarks, PDF4LHC Meeting, CERN - Geneve, Switzerland |
| july 2008 |
Benchmark Partons, PDF4LHC Meeting, CERN - Geneve, Switzerland |
| may 2008 |
NNPDF Benchmark Partons, HERA and the LHC Workshop, CERN - Geneve, Switzerland |
| april 2008 |
Re-evaluation of the Bjorken sum rule with a MonteCarlo approach, DIS2008, UCL - London, UK |
| june 2006 |
Latest results on PDFs uncertainties with Neural Networks, HERA and the LHC Workshop, CERN - Geneve, Switzerland |
| may 2006 |
The neural network approach to parton fitting, Universita' di Torino. |
| april 2006 |
The neural network approach to parton fitting: the non-singlet case, DIS 2006 XIV International Workshop on Deep Inelastic Scattering, Tsukuba, Japan. |
| february 2006 |
The neural network approach to pdf fitting, SLAC - Stanford, USA. |
| january 2006 |
The neural network approach to pdf fitting, University of Edimburgh, UK. |
| october 2005 |
Neural Networks and the Structure of the Proton, Highlights in Physics 2005, Universita' di Milano. |
| july 2005 |
Exploring the structure of the nucleon with Neural Networks, INFN Genova. |
| may 2005 |
Neural networks approach to parton distributions fitting, X International Workshop on Advanced Computing and Analysis Techniques in Physics Research, DESY - Zeuthen, Germany.
|
| march 2005 |
Recent progress on neural PDFs, HERA and the LHC Workshop, DESY - Hamburg, Germany. |
