Prototype of machine learning “as a service” for CMS physics in signal vs background discrimination

Giommi, Luca (2018) Prototype of machine learning “as a service” for CMS physics in signal vs background discrimination. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270]
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Abstract

Big volumes of data are collected and analysed by LHC experiments at CERN. The success of this scientific challenges is ensured by a great amount of computing power and storage capacity, operated over high performance networks, in very complex LHC computing models on the LHC Computing Grid infrastructure. Now in Run-2 data taking, LHC has an ambitious and broad experimental programme for the coming decades: it includes large investments in detector hardware, and similarly it requires commensurate investment in the R&D in software and com- puting to acquire, manage, process, and analyse the shear amounts of data to be recorded in the High-Luminosity LHC (HL-LHC) era. The new rise of Artificial Intelligence - related to the current Big Data era, to the technological progress and to a bump in resources democratization and efficient allocation at affordable costs through cloud solutions - is posing new challenges but also offering extremely promising techniques, not only for the commercial world but also for scientific enterprises such as HEP experiments. Machine Learning and Deep Learning are rapidly evolving approaches to characterising and describing data with the potential to radically change how data is reduced and analysed, also at LHC. This thesis aims at contributing to the construction of a Machine Learning “as a service” solution for CMS Physics needs, namely an end-to-end data-service to serve Machine Learning trained model to the CMS software framework. To this ambitious goal, this thesis work contributes firstly with a proof of concept of a first prototype of such infrastructure, and secondly with a specific physics use-case: the Signal versus Background discrimination in the study of CMS all-hadronic top quark decays, done with scalable Machine Learning techniques.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Giommi, Luca
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
Curriculum B: Fisica nucleare e subnucleare
Ordinamento Cds
DM270
Parole chiave
Machine Learning,High Energy Physics,CMS,Top physics
Data di discussione della Tesi
23 Marzo 2018
URI

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