FI: MACHINE LEARNING IN HIGH ENERGY PHYSICS - TOMMASO BOCCALI 
 ELENCO COMPLETO 

DATA: 10-12-2019

SEZIONE DI FIRENZE
Human brain inspired techniques (Machine Learning or ML) are flourishing in research environments, taking advantage of recent important theoretical results, of the availability of specialised hardware and the needs for novel solutions for the appearance of problems apparently intractable with standard approaches. High Energy Physics is a field where ML has been used as a promising and intensively exploited tool, with studies performed at all levels of the activities, ranging from applications to data acquisition to offline data analysis and allowing to optimize algorithms and the use of resources, especially in the identification of events and physics objects. In order to illustrate the variety and richness of this established research topic we start from a brief review of the landscape and its evolution and continue presenting a number of selected applications and R&D projects for both current and future HEP experiments.(Tommaso Boccali)


 SITO COLLEGATO 

 EVENTI RECENTI 
16-01-2020: The universe acceleration in modified gravity: an overview
16-01-2020: A. A. Bergamini Machado, E. Segreto - The ARAPUCA and the Photon Detection System of the DUNE and SBND experiments
15-01-2020: AstroPisa JC
10-01-2020: Seminario di Pierluigi Bortignon: Search for the Higgs boson in its decays to the second-generation fermions
09-01-2020: Graphene Wormholes: From General Relativity to Nano-technologies
07-01-2020: Progetto di Alternanza Scuola Lavoro "Quantum Physics at Tor Vergata"
20-12-2019: "La flessibilità del tempo" - Caffè Scientifico con Silvia Miozzi
19-12-2019: Searching CLFV and LNV processes with the Mu2e experiment
19-12-2019: Exceptionally Heavy Dark Matter - Noam Levi
18-12-2019: MisuraCC3M@LNL: plasmi

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