LABORATORY OF COMPUTATIONAL AND STATISTICAL METHODS

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Code
90741
ACADEMIC YEAR
2021/2022
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
Prerequisites
You can take the exam for this unit if you passed the following exam(s):
  • PHYSICS 8758 (coorte 2019/2020)
  • PHYSICS II 57049
  • LABORATORY 1 90736
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

Office hours: 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

Office hours: 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

LESSONS

TEACHING METHODS

Lectures and laboratory exercises.

Class schedule

All class schedules are posted on the EasyAcademy portal.

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

Date Time Location Type Notes