CODE 84470 ACADEMIC YEAR 2017/2018 CREDITS 6 cfu anno 3 FISICA 8758 (L-30) - 6 cfu anno 2 FISICA 9012 (LM-17) - SCIENTIFIC DISCIPLINARY SECTOR FIS/01 LANGUAGE Italian TEACHING LOCATION SEMESTER 1° Semester PREREQUISITES Propedeuticità in ingresso Per sostenere l'esame di questo insegnamento è necessario aver sostenuto i seguenti esami: PHYSICS 8758 (coorte 2015/2016) PHYSICS II 57049 2015 LABORATORY 2 66576 2015 PHYSICS 8758 (coorte 2016/2017) PHYSICS II 57049 2016 LABORATORY 1 90736 2016 TEACHING MATERIALS AULAWEB OVERVIEW The Computational Physics Laboratory 2 Course (LFC2, code 84470) is worth 6 credits and takes place in the first semester of the following years: 3rd L-30; 2nd LM-17. Classes are held in Italian (English under request). AIMS AND CONTENT LEARNING OUTCOMES The course provides numerical analysis methods for physics problems and advanced methods of data analysis. 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). 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 STEFANO PASSAGGIO Exam Board FABRIZIO PARODI (President) STEFANO PASSAGGIO LESSONS Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Brief thesis on specific topic and oral exam. During the oral discussion of the thesis will be followed by questions on the remaining part of the program. ASSESSMENT METHODS The exam foresees the preparation of a brief thesis on a specific subject and an oral exam. The thesis is aimed at ascertaining the acquisition of the skills provided by the course and the ability to apply them to physics problems. The oral consists of the discussion of the thesis and questions concerning the remaining part of the program. Exam schedule Data appello Orario Luogo Degree type Note 25/01/2018 09:00 GENOVA Orale 15/02/2018 09:00 GENOVA Orale