Catalogo dei Corsi di studio

GENERAL OBJECTIVES:
The aim of the course is to present the key elements of the design of experiments in particle physics and the main analysis methods of the collected data in order to get the physics results.
A selection of historycal and recent experiments is considered and discussed.
At the end of the course, the student is able to understand and discuss research papers about measurements of the experiments and has acquired concepts and strategies needed for data analysis, for instance, to do a thesis in experimental particle physics.

SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To know the methods of statistical data analysis, fit and hypothesis test used in a particle physics experiment.
OF 2) To understand the key design aspects for the measurement of an observable in a particle physics experiment.
OF 3) To know the main strategies for event selection, background rejection and control of efficiencies in a particle physics experiment.
B - Application skills
OF 4) To know how to implement the appropriate data selection and analysis strategies for the measurement of an observable in particle physics.
C - Autonomy of judgment
OF 5) To be able to integrate the knowledge acquired in order to apply them in the context of any experiment in particle physics.
D - Communication skills

E - Ability to learn
OF 6) Have the ability to read scientific papers in order to further explore some of the topics introduced during the course.

Programme

1. Historical introduction, example of scattering experiment, units,
energy scales, fundamental interactions, cross section, lifetime and estimate of orders of magnitude.

2. The logic of an experiment in particle physics.
Selection and reduction of data, trigger, offline reconstruction, analysis.
Counting of events, introduction to normalization, efficiency, resolution and background evaluation.

3. The quantities to be measured in particle physics with examples.
Cross section, Branching ratio, asymmetry, mass, width and lifetime of the elementary particles.
Aspects of measurement related to the beam: flux, luminosity, pile-up.
The aspects of the measurement related to the detector: geometric acceptance, efficiency, resolution, convolution and deconvolution techniques, estimation and subtraction of the background.
Monte Carlo simulation, theory and examples. Data fit.
Introduction to discriminant and multivariate analysis.
Absolute and relative measurements, evaluation of systematic uncertainties.

4. The language of random variables and statistical inference.
Review of fundamental concepts in statistics and the main probability distributions. Statistical and systematic uncertainties.
Analysis of event distributions, likelihood, fit, choice of test statistics, parameter estimation, confidence intervals, frequentist and Baysian approach.
Examples of signal fit and signal + background.
Signal search, upper and lower limits in the frequentist and Bayesian approaches. CLs method. The look-elsewhere effect. The example of the observation of the Higgs boson.
Kinematic Fit.

5. Examples of detector design and measurements with hadron and e + e- colliders, fixed target experiments, neutrino beams, underground or satellite experiments.
Topical seminars on measurements of experiments or advanced data analysis techniques.

Adopted texts

1) available material on the course web site:
https://elearning.uniroma1.it

- Notes on data analysis in particle physics and advanced statistical methods.
- Collection of excercises with solutions on topics discussed in the course.
- Slides presented during the lectures.
- List of original research papers with examples of measurements performed by experiments in the field of particle physics.

2) G. Cowan, Statistical Data Analysis, Oxford Science Publications (1998)

3) Original research papers with examples of measurements performed by experiments in the field of particle physics

Bibliography

1) G. Cowan, Statistical Data Analysis, Oxford Science Publications (1998) 2) L. Lista, Statistical Methods for Data Analysis in Particle Physics, Springer (2018) 3) G. D’Agostini, Bayesian reasoning in data analysis, World Scientific (2005) 4) Robert N. Cahn, Gerson Goldhaber "The Experimental Foundations of Particle Physics", 2nd Edition, Cambridge University Press (2009) 5) M. Tanabashi et al. (Particle Data Group), The Review of Particle Physics, Phys. Rev. D 98, 030001 (2018).

Prerequisites

a) A fundamental prerequisite is the knowledge requested by the first level University degree in Physics. Specific competences are requested in classical physics, nuclear and subnuclear physics, quantum mechanics, special relativity, computing laboratory, basic elements of probability and statistics. b) It is important that students have basic knowledge of radiation matter interactions and particle detectors.

Exam modes

The examination consists of an interview on the most relevant topics
presented in the course, and in the solution of proposed exercises.
Moreover it is requested to illustrate in detail
a measurement of an experiment in the field of particle physics
described in a specialized paper.

To pass the exam, the student must be able to present the arguments in general and in their application to the discussed specific case according to the methods learned in exercises or examples and situations similar to those that were discussed in the course.

The evaluation takes into account:
- Correctness and completeness of the concepts discussed by the
student;
- clarity and rigor of presentation;
- analytical development of the theory;
- problem-solving skills (method and results).

Exercises with numerical solutions and written tests proposed during the course complement the final evaluation.

Exam reservation date start	Exam reservation date end	Exam date
01/01/2022	09/01/2022	10/01/2022
18/01/2022	30/01/2022	31/01/2022
01/05/2022	15/05/2022	16/05/2022
01/06/2022	19/06/2022	20/06/2022
21/06/2022	03/07/2022	04/07/2022
01/09/2022	18/09/2022	19/09/2022
01/11/2022	13/11/2022	14/11/2022