Oct 20 – 25, 2019
America/Mexico_City timezone

Studying the parton content of the proton with deep learning models

Oct 21, 2019, 3:10 PM
20m
Oral Physics and astronomy Submitted contributions

Speaker

Dr Juan Manuel Cruz Martínez (University of Milan)

Description

Parton Distribution Functions (PDFs) model the parton content of the proton. Of the many collaborations which focus on PDF determination in the last 20 years, NNPDF was pioneer on the use of Neural Networks to model the probability of finding partons (quarks and gluons) inside the proton with a given energy and momentum.

In this work we introduce state of the art techniques to modernize the NNPDF methodology and study different models and architectures in a systematic way which allows as to assess the quality of the PDF fit: overtraining, model complexity, hyperparameter setup, optimization algorithm...

We show a fully automatized pipeline to achieve a best model setup finding good improvements in both the quality and efficiency of the fits.

Primary authors

Dr Juan Manuel Cruz Martínez (University of Milan) Dr Stefano Carrazza (University of Milan)

Presentation materials