Design of a beam stopper for the new CERN linear accelerator Linac4

In this paper I will present the work I have completed during a five months work placement at CERN, European Organisation for Nuclear Research, from March to July 2011. This stage was done in the EN Department (ENgineering Department), STI Group (Sources, Targets and Interactions), TCD Section (Targets, Collimators and Dumps) under the supervision of Dr Cesare Maglioni. The task I was given concerned all the beam stoppers in the PS Complex, in detail: - General definition and requirements - Creation of a digital archive - Verification of the stoppers of the PS Complex - Design of the L4T.STP.1

Cattura neutronica su u-238: Studio della regione delle risonanze risolte.

Le attuali sezioni d’urto di reazioni indotte da neutroni non sono sufficientemente accurate per essere utilizzate in progetti di ricerca di frontiera. Per soddisfare la richiesta di nuovi dati nucleari è stata recentemente costruita al CERN di Ginevra la facility n_TOF (neutron Time Of Flight). Le caratteristiche che contraddistinguono questa facility sono l’elevato flusso istantaneo di neutroni, l’ampio spettro energetico e l’eccellente risoluzione energetica. In questa tesi viene analizzata la sezione d’urto di cattura neutronica su 238U, fondamentale per la realizzazione dei reattori nucleari di nuova generazione che prevedono la sostituzione del combustibile 235U con 238U, molto più abbondante in natura e che permette di ridurre drasticamente l’accumulo di scorie nucleari. Nonostante le numerose misure già effettuate su questo elemento, ancora non si è raggiunta la precisione necessaria per le tecnologie nucleari emergenti quali i reattori di quarta generazione e gli ADS (Accelerator Drive System). Inoltre la parametrizzazione della sezione d’urto in termini di matrice R, non è così accurata quanto richiesto. In questo lavoro si è dapprima studiata l’assegnazione dello spin delle risonanze riportata in letteratura, elemento fondamentale per la successiva analisi delle risonanze e la parametrizzazione della sezione d’urto della reazione 238U(n, ). Parallelamente a questa attività si è studiata la funzione di risposta dello spettrometro n_TOF, che descrive la risoluzione energetica dei neutroni incidenti e va quindi a modificare la forma delle risonanze. I risultati ottenuti sono quindi stati confrontati con quelli in letteratura per poter essere validati e per poter verificare eventuali migliorie.

Multivariate analysis methods in the search for the Higgs boson produced in association with top pairs at the ATLAS experiment at LHC.

The Large Hadron Collider, located at the CERN laboratories in Geneva, is the largest particle accelerator in the world. One of the main research fields at LHC is the study of the Higgs boson, the latest particle discovered at the ATLAS and CMS experiments. Due to the small production cross section for the Higgs boson, only a substantial statistics can offer the chance to study this particle properties. In order to perform these searches it is desirable to avoid the contamination of the signal signature by the number and variety of the background processes produced in pp collisions at LHC. Much account assumes the study of multivariate methods which, compared to the standard cut-based analysis, can enhance the signal selection of a Higgs boson produced in association with a top quark pair through a dileptonic final state (ttH channel). The statistics collected up to 2012 is not sufficient to supply a significant number of ttH events; however, the methods applied in this thesis will provide a powerful tool for the increasing statistics that will be collected during the next LHC data taking.

Study of the performances of the H->ZZ*->4l analysis with the CMS phase-II upgrade

The performances of the H → ZZ* → 4l analysis are studied in the context of the High Luminosity upgrade of the LHC collider, with the CMS detector. The high luminosity (up to L = 5 × 10^34 cm−2s−1) of the accelerator poses very challenging experimental con- ditions. In particular, the number of overlapping events per bunch crossing will increase to 140. To cope with this difficult environment, the CMS detector will be upgraded in two stages: Phase-I and Phase-II. The tools used in the analysis are the CMS Full Simulation and the fast parametrized Delphes simulation. A validation of Delphes with respect to the Full Simulation is performed, using reference Phase-I detector samples. Delphes is then used to simulate the Phase-II detector response. The Phase-II configuration is compared with the Phase-I detector and the same Phase-I detector affected by aging processes, both modeled with the Full Simulation framework. Conclusions on these three scenarios are derived: the degradation in performances observed with the “aged” scenario shows that a major upgrade of the detector is mandatory. The specific upgrade configuration studied allows to keep the same performances as in Phase-I and, in the case of the four-muons channel, even to exceed them.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.