Objectif "The objective of this project is to revolutionize the way the structure of the proton is accessed, determined, and used in the computation of physical processes at hadron colliders such as the Large Hadron Collider (LHC) of CERN. At a hadron accelerator, predictions require a precise, detailed, and accurate description and understanding of the structure of the colliding protons, as encoded in parton distributions (PDFs) - the distributions of quarks and gluons. At the LHC, PDFs are at present the major source of uncertainty, and in the near future they will be the main hurdle for discovery. The vision of this project is to remove this hurdle by attacking the problem using recent results from artificial intelligence (AI). I will lead a research team of two staff scientists, four postdocs and three PhD students, who will apply to PDF determination the recent methods of deep reinforcement learning and Q-learning, which will be coupled with deep residual networks to achieve a fully parameter- and bias-free understanding of proton structure. I will bring into high-energy physics a methodology so far used for object recognition in self-driving cars and automatic game playing, leading both to new physics, and new computational techniques. The application of these techniques to PDFs will enable me to reach two secondary goals. The first is theoretical: the full use for PDF determination of recent high perturbative order (next-to-next-to leading order or NNLO) computations, which will be integrated by means of a new approximation method which relies on combining known exact results with all-order information in various kinematic limits to extend the scope of the former to a more detailed (""more exclusive"") description of the final state.The second is phenomenological: the integration in PDF determination of the Monte-Carlo event generators which are used to turn field theoretical prediction into a realistic description which may be directly compared to experimental data." Champ scientifique natural sciencescomputer and information sciencesartificial intelligencenatural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learningnatural sciencesphysical sciencestheoretical physicsparticle physicsgluonsnatural sciencesphysical sciencestheoretical physicsparticle physicsquarks Mots‑clés Standard Model Large Hadron Collider Quantum Chromodynamics Parton Distributions Structure Functions Artificial Intelligence Neural Networks Deep Learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Thème(s) ERC-2016-ADG - ERC Advanced Grant Appel à propositions ERC-2016-ADG Voir d’autres projets de cet appel Régime de financement ERC-ADG - Advanced Grant Institution d’accueil UNIVERSITA DEGLI STUDI DI MILANO Contribution nette de l'UE € 1 602 862,00 Adresse Via Festa Del Perdono 7 20122 Milano Italie Voir sur la carte Région Nord-Ovest Lombardia Milano Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 1 602 862,00 Bénéficiaires (1) Trier par ordre alphabétique Trier par contribution nette de l'UE Tout développer Tout réduire UNIVERSITA DEGLI STUDI DI MILANO Italie Contribution nette de l'UE € 1 602 862,00 Adresse Via Festa Del Perdono 7 20122 Milano Voir sur la carte Région Nord-Ovest Lombardia Milano Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 1 602 862,00
