LABORATORIO DI METODI COMPUTAZIONALI E STATISTICI

LABORATORIO DI METODI COMPUTAZIONALI E STATISTICI

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Last update 02/07/2020 18:32
Code
90741
ACADEMIC YEAR
2020/2021
CREDITS
6 credits during the 3nd year of 8758 PHYSICS (L-30) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
FIS/01
LANGUAGE
Italian
TEACHING LOCATION
GENOVA (PHYSICS)
semester
1° Semester
Prerequisites
Teaching materials

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

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

Exam Board

FABRIZIO PARODI (President)

STEFANO PASSAGGIO

ROBERTA CARDINALE (President Substitute)

LESSONS

Teaching methods

Lectures and laboratory exercises.

ORARI

L'orario di tutti gli insegnamenti è consultabile su 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.