EXPERIMENTAL METHODS FOR NUCLEAR PHYSICS

Apprendere le principali metodologie sperimentali per l’analisi dei dati da esperimenti di fisica nucleare

Corsi introduttivi di Fisica Nucleare

obbligatoria

Programma del corso:

Experimental errors in nuclear physics

Error estimation - Statistical errors – Statistics of counting – Mean and variance - Systematic errors – Examples from realistic nuclear physics experiments – Combining results from different experiments – Significance tests

Probability distributions and their use in nuclear physics

Probability – Parameter estimation – Hypothesis testing – Binomial distribution – Poisson distribution – Gaussian distribution – Landau-Vavilov distribution and the high energy tail in the energy loss of charged particles - Other distributions of interest for nuclear physics - Application to specific nuclear physics phenomena – Particle counting – Time between successive random events – Dead time of a detector – Occupancy for a segmented detector – Photoelectrons in a photomultiplier - Uncorrelated variables – Distributions in two or more dimensions – Error matrix manipulation

Parameter fitting

Parameter estimation – Interpretation of estimates – The method of moments – Maximum likelihood method – Least squares method – Minimization processes – Examples of fitting procedures – Straight line fit with errors on x and y – Kinematic fitting – Use of constraints – Hypothesis testing

Background subtraction

Invariant mass spectra – Estimation of combinatorial background – Fit by smooth mathematical functions – Combinatorial background – Methods and algorithms for background subtraction in high multiplicity events - The event mixing method - The track rotation method – The like sign method

Methods for data acquisition and analysis

Multiparametric data acquisition systems – Multiparametric analysis - Trigger design and event selection – Event filtering – Classification of events by centrality and reaction plane determination -

Tracking and pattern recognition methods

Pattern recognition methods – Hough transform and its application to RICH detectors – Tracking methods – Track recognition and reconstruction – Simple combinatorial methods - Primary and secondary vertex finding – Kalman Filter method – Shower analysis for calorimeters – Shape analysis – Jet reconstruction

Neural network methods

Artificial neural networks (ANN) – Implementation of ANN by the ROOT package - Applications of ANN to problems in nuclear physics: particle identification, particle tracking, signal reconstruction, forecast methods.

Monte Carlo methods and detector simulation

Basic of Monte Carlo methods – Random numbers and sequences - Monte Carlo methods and application to nuclear physics – Simulation techniques for the evaluation of detector properties - Detector acceptance and efficiency - Simulation of physical processes and detector response – Implementation and use of simulation codes – The GEANT tool – Examples and applications in nuclear physics and related areas

L.Lyons, Statistics for nuclear and particle physicists, Cambridge University Press

Useful additional references for the topics discussed will be provided along the lectures.

Tutto il materiale didattico del corso (lezioni, esercitazioni, tutorial,...) è disponibile sul sito:

lab3ct.altervista.org

Presentazione di una tesina scritta che riporti un lavoro di analisi/simulazione condotto con qualcuno dei metodi illustrati durante il corso

Discussione orale

Non sono previste prove in itinere

Non sono previste prove di fine corso se non quelle d'esame

Metodi di simulazione Monte Carlo - SImulazione con package GEANT - Reti neurali - Digital Pulse Processing