HIGH ENERGY NUCLEAR PHYSICS

Academic Year 2020/2021 - 2° Year - Curriculum NUCLEAR AND PARTICLE PHYSICS
Teaching Staff: Francesco RIGGI
Credit Value: 6
Scientific field: FIS/04 - Nuclear and subnuclear physics
Taught classes: 42 hours
Term / Semester:

Learning Objectives

Learn the main experimental methods in data analysis for nuclear physics experiments

With reference to "Dublin descriptors", this Course contributes to provide the following skillness:

  • Ability in induction and deduction methods
  • Capability to learn and evaluate experimental results in nuclear physics by reading scientific papers in the field.
  • Capability to setup and define a problem by using quantitative relations (algebraic, differential, integral) between phyisical variables and to solve it by means of analystical or numerical algorithms.
  • Capability to carry out statistical analyses of results.
  • Capability to perform analysis sessions of experimental data from nuclear physics experiments.

Capability to apply the knowledge in order to:

  • Describe physical phenomena by a correct and quantitative application of scientific methodologies.
  • Evaluate the performance of experiments in nuclear physics and carry out the analysis of experimental data.
  • Perform numerical calculations and simulation procedures.

Autonomy of judgment:

  • Reasoning skills.
  • Capability to find the most appropriate methods for a critical evaluation and interpretation of experimental data.
  • Capability to understand the prediction of a model or theory.
  • Capability to evaluate the accuracy and importance of existing measurements.
  • Capability to evaluate the goodness and limits in the comparison between experimental data and theoretical predictions.

Communication skills:

  • Capability to appropriately communicate scientific topics and problems, discussing the motivations and main results.
  • Capability to describe in a written report a scientific topic or problem, discussing the motivations and main results.

Course Structure

1) Lectures

2) Numerical exercises

3) Data analysis sessions

All activities will be carried out in English.

Should the circumstances require online or blended teaching, appropriate modifications to what is hereby stated may be introduced, in order to achieve the main objectives of the course.

Exams may take place online, depending on circumstances.


Detailed Course Content

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.

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 – Global variables and centrality evaluation – Determination of reaction plane - Event splitting and evaluation of errors.

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 – Use of neural network algorithms for classification.

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

Digital Pulse Processing: Signal processing with standard electronics – Working principles of ADC, Discriminators and TDC - Digitizer and digital oscilloscopes - Offline analysis of digital signals – Methods and algorithms for digital pulse processing – Examples.

Analysis sessions of experimental data from LHC experiments: Structure of a reduced tree from reconstructed data – Implementation of a readout ROOT macro – Simple basic analyses: Track multiplicity distribution - Inclusive single particle spectra – Transverse momentum and pseudorapidity spectra – Quality of tracks and track selection – Particle identification – Identified particle spectra – V0 selection – Invariant mass analysis – Reconstruction of K0s from pion pairs – Reconstruction of Λ and its antiparticle – Armenteros plot.


Textbook Information

1) C.Wong, Introduction to Heavy Ion collisions, World Scientific.

2) R.Vogt, Ultrarelativistic heavy ion collisions, Elsevier.

3) G.F.Knoll, Radiation Detection and Measurements, Wiley.

Further specific references will be provided during the lectures.