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COMPUTATIONAL PHYSICS LAB 2

CODE 84470
ACADEMIC YEAR 2017/2018
CREDITS 6 credits during the 3nd year of 8758 PHYSICS (L-30) GENOVA

6 credits during the 2nd year of 9012 PHYSICS (LM-17) 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 2015/2016)
  • PHYSICS II 57049
  • LABORATORY 2 66576
  • PHYSICS 8758 (coorte 2016/2017)
  • PHYSICS II 57049
  • LABORATORY 1 90736
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

Exam Board

FABRIZIO PARODI (President)

STEFANO PASSAGGIO

LESSONS

TEACHING METHODS

Lectures and laboratory exercises.

Class schedule

All class schedules are posted on the EasyAcademy portal.

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

Date Time Location Type Notes
25/01/2018 09:00 GENOVA Orale
15/02/2018 09:00 GENOVA Orale