EXPERIMENTAL METHODS FOR NUCLEAR PHYSICS

Anno accademico 2017/2018 - 1° anno - Curriculum NUCLEAR AND PARTICLE PHYSICS e Curriculum NUCLEAR PHENOMENA AND THEIR APPLICATIONS
Docente: Francesco RIGGI
Crediti: 6
SSD: FIS/01 - FISICA SPERIMENTALE
Organizzazione didattica: 150 ore d'impegno totale, 108 di studio individuale, 42 di lezione frontale
Semestre:

Obiettivi formativi

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


Prerequisiti richiesti

Corsi introduttivi di Fisica Nucleare


Frequenza lezioni

obbligatoria


Contenuti del corso

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


Testi di riferimento

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

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



Programmazione del corso

 *ArgomentiRiferimenti testi
1*Un lavoro personalizzato di analisi che utilizzi qualcuno dei metodi illustrati durante il corso 
* Conoscenze minime irrinunciabili per il superamento dell'esame.

N.B. La conoscenza degli argomenti contrassegnati con l'asterisco è condizione necessaria ma non sufficiente per il superamento dell'esame. Rispondere in maniera sufficiente o anche più che sufficiente alle domande su tali argomenti non assicura, pertanto, il superamento dell'esame.

Verifica dell'apprendimento

Modalità di verifica dell'apprendimento

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

Discussione orale


Esempi di domande e/o esercizi frequenti

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