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LABORATORY OF COMPUTATIONAL AND STATISTICAL METHODS

CODE 90741
ACADEMIC YEAR 2019/2020
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 2017/2018)
  • PHYSICS II 57049
  • LABORATORY 1 90736
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

Exam Board

FABRIZIO PARODI (President)

ROBERTA CARDINALE

STEFANO PASSAGGIO

LESSONS

TEACHING METHODS

Lectures and laboratory exercises.

Class schedule

All class schedules are posted on the EasyAcademy portal.

EXAMS

EXAM DESCRIPTION

Computer-based exam. Brief thesis on a specific topic.

ASSESSMENT METHODS

The exam includes a computer test aimed at ascertaining the acquisition of the computational and statistics skills provided by the course.
The thesis aims at verifying the ability to apply computational and statistical techniques to physical problems.

Exam schedule

Date Time Location Type Notes
15/01/2020 09:00 GENOVA Laboratorio
07/02/2020 09:00 GENOVA Laboratorio
05/06/2020 09:00 GENOVA Laboratorio
03/07/2020 09:00 GENOVA Laboratorio
18/09/2020 09:00 GENOVA Laboratorio