CODE 90741 ACADEMIC YEAR 2021/2022 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 2019/2020) PHYSICS II 57049 2019 LABORATORY 1 90736 2019 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 Ricevimento: Reception to be agreed upon e-mail / MS Teams contact. Roberta Cardinale Department of Physics, via Dodecaneso 33, 16146 Genoa floor 8, studio 817 e-mail: roberta.cardinale@ge.infn.it 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 and oral exam. Practical exercises during the course will contribute to the final score (up to 3 additional points) 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 12/01/2022 09:00 GENOVA Laboratorio 10/02/2022 09:00 GENOVA Laboratorio 07/06/2022 09:00 GENOVA Laboratorio 05/07/2022 09:00 GENOVA Laboratorio 16/09/2022 09:00 GENOVA Laboratorio
