CODE 90741 ACADEMIC YEAR 2020/2021 CREDITS 6 cfu anno 3 FISICA 8758 (L-30) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR FIS/01 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 1° Semester PREREQUISITES Propedeuticità in ingresso Per sostenere l'esame di questo insegnamento è necessario aver sostenuto i seguenti esami: PHYSICS 8758 (coorte 2018/2019) PHYSICS II 57049 2018 LABORATORY 1 90736 2018 TEACHING MATERIALS AULAWEB OVERVIEW Computational and Statistical Methods Laboratory (LMCS, code 90741) is worth 6 credits and takes place in the first semester of the 3rd year of the three-year degree (L-30) AIMS AND CONTENT LEARNING OUTCOMES The course aims to consolidate and expand the skills of calculation, statistical analysis and programming, aimed at analyzing and acquiring data in laboratory experiences. AIMS AND LEARNING OUTCOMES The course deals with computational physics, addressing the numerical solution of ordinary differential equations and partial derivatives, and advanced methods of data analysis, dealing with Monte Carlo simulation rudiments, deepening the best-fit techniques and giving an overview of multivariate signal/background separation techniques. The course also aims to extend the C++ programming skill (acquired during the first year) by more in-depth study of Object-Oriented programming in C ++ and providing rudiments of Python and shell scripting. Higher level packages will be also used (ROOT, Octave / Matlab). PREREQUISITES The course assume that computation skills contained in the first year coures are acquired. TEACHING METHODS Lectures and laboratory exercises. SYLLABUS/CONTENT Lectures schedule: OO programming (inheritance, polymorphism), shell scripting and Python, use of specific packages / libraries (ROOT, Octave / Matlab) Numerical solution of ordinary differential equations. Applications to classical physics and quantum mechanics problems (Numerov method for the Schrodinger equation). Numerical solution of partial differential equations. Applications to eletromagnetism and heat propagation. Introduction to the generation of random variables and Monte Carlo simulation Extraction of quantities of interest from a data sample: binned and unbinned likelihood. Point estimate, confidence intervals. Hypothesis testing. Limits. Overview of multivariate classification techniques (Likelihood ratio, neural networks). RECOMMENDED READING/BIBLIOGRAPHY Notes / slides are provided during the course. A list of possible texts for further information is available on the course page on Aulaweb. TEACHERS AND EXAM BOARD FABRIZIO PARODI Ricevimento: Reception to be agreed upon telephone / e-mail contact. Fabrizio Parodi Department of Physics, via Dodecanese 33, 16146 Genoa Office 823, Telephone 010 3536657 e-mail: fabrizio.parodi@ge.infn.it ROBERTA CARDINALE STEFANO PASSAGGIO Exam Board FABRIZIO PARODI (President) STEFANO PASSAGGIO ROBERTA CARDINALE (President Substitute) LESSONS Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Computer-based exam. Practical exercises during the course will contribute to the final score. ASSESSMENT METHODS The exam includes a computer test aimed at ascertaining the acquisition of the computational and statistics skills provided by the course. Practical exercises during the course will contribute to the final score. Exam schedule Data appello Orario Luogo Degree type Note 13/01/2021 09:00 GENOVA Laboratorio 05/02/2021 09:00 GENOVA Laboratorio 04/06/2021 09:00 GENOVA Laboratorio 02/07/2021 09:00 GENOVA Laboratorio 17/09/2021 09:00 GENOVA Laboratorio