EXPERIMENTAL METHODS FOR NUCLEAR PHYSICS

The course aims to introduce advanced detection techniques and the main experimental methodologies used in nuclear physics for the simulation, signal processing, and analysis of data from nuclear physics experiments, also through practical exercises.

With reference to the so-called Dublin Descriptors, the course contributes to the acquisition of the following transferable skills:

Knowledge and understanding

• Ability to use inductive and deductive reasoning.
• Ability to understand the principles underlying the main detection techniques and experimental methodologies used in nuclear physics.
• Ability to learn from and critically assess experimental results through the study of specialized scientific literature.
• Ability to formulate and solve physical problems using analytical or numerical methods.
• Ability to perform statistical analysis of real and simulated data.

Applying knowledge and understanding

• Ability to apply the acquired knowledge to the description of physical phenomena using the scientific method rigorously.
• Ability to apply detection, signal processing, simulation, and data analysis methodologies to nuclear physics experiments.
• Ability to assess the performance of experiments and detectors and to analyze experimental data.
• Ability to perform numerical calculations and simulations.

Making judgements

• Ability to apply critical reasoning.
• Ability to identify the most appropriate methods for analyzing, interpreting, and processing experimental data.
• Ability to critically assess the accuracy and relevance of measurements and results available in the scientific literature.
• Ability to assess the quality and limitations of comparisons between experimental data, simulations, and theoretical models.

Communication skills

• Ability to present a scientific topic orally, using appropriate language and terminology, and clearly illustrating its motivations and results.
• Ability to describe an experimental activity and its results in written form, using appropriate language and terminology.

Learning skills

• Ability to independently deepen the knowledge of experimental methodologies and data analysis techniques introduced during the course, also through the study of scientific literature.
• Ability to independently learn new techniques for detection, signal processing, simulation, and data analysis.
• Ability to apply the acquired knowledge and methodologies to experimental problems not directly addressed during the course.

The course consists of 6 CFU credits, for a total of 21 hours of lectures and 45 hours of practical activities, including exercises, data analysis sessions, and laboratory activities.

Different teaching methods will be employed throughout the course, aimed at acquiring and applying experimental methodologies in nuclear physics:

• Lectures, aimed at providing the theoretical knowledge underlying detection techniques, data acquisition and signal processing, as well as the main data analysis and simulation methodologies.
• Numerical exercises, aimed at applying the acquired knowledge and developing the ability to address problems using analytical and numerical methods.
• Data analysis and simulation sessions, focused on the practical application of techniques for processing and analyzing real and simulated data, as well as on the simulation of detectors and experimental setups.
• Laboratory activities, aimed at developing practical skills in detection techniques, data acquisition and signal processing, and at assessing the performance of experimental systems.

The activities will be conducted in English.

If the course is delivered in blended or remote mode, appropriate adjustments may be made to the above, in order to ensure consistency with the syllabus.

Essential prerequisites:

• Introductory courses in Nuclear Physics
• Knowledge of statistics and experimental data processing
• Basic computer skills

Recommended prerequisites:

• Knowledge of the ROOT analysis framework or Python

Attendance is strongly recommended (see the Teaching Regulations of the Degree Programme), given the highly practical nature of the course and the importance of practical exercises and laboratory activities for acquiring the expected skills.

The course follows a path that, starting from detection techniques and data acquisition and signal processing systems, introduces trigger, data analysis and reconstruction methodologies, applications of artificial intelligence, and simulation techniques, leading to their application through practical exercises and experimental activities using real and simulated data.

Detection Techniques and Data Acquisition

• Advanced detectors: overview of solid-state and gaseous detectors; operating principles and examples of modern detectors, including CCDs, strip detectors, MAPS and hybrid pixel detectors, CMOS, LGAD, TPC, GEM, and MRPC. Additional detection technologies may be introduced based on students’ interests.
• Data acquisition and trigger systems: multiparameter data acquisition, data transmission and management, trigger and event selection systems, trigger-less systems, and applications of artificial intelligence to online data processing.
• Digital Pulse Processing: operating principles of ADCs, discriminators, and TDCs; digitizers and digital oscilloscopes; digitization and offline analysis of signals; methods and algorithms for digital pulse processing.
• Synchronization and coincidence measurements: coincidence measurements between detectors using conventional techniques and GPS-based synchronization systems.

Data Analysis and Reconstruction Methodologies

• Background subtraction: invariant mass spectra, combinatorial background, fitting with continuous functions, and main methods and algorithms for background subtraction, including *event mixing*, *like-sign*, and *rotated-track* techniques.
• Tracking and reconstruction: tracking and pattern recognition algorithms, reconstruction of primary and secondary vertices, and introduction to the Kalman Filter method.
• Neural networks and artificial intelligence: principles of artificial neural networks (ANNs) and their applications in nuclear physics, with particular emphasis on particle identification and tracking, signal reconstruction, classification problems, and prediction methods.
• Monte Carlo methods and simulation: principles and techniques of simulation, simulation of detector properties and response, and introduction to the main simulation codes used in nuclear physics and related fields.

Practical Exercises and Experimental Activities

The methodologies introduced during the course will be applied through practical data analysis sessions and experimental activities, including:

• development and application of algorithms for the processing and analysis of digitized signals;
• reconstruction of primary and secondary vertices and analysis of invariant mass spectra, including the study and subtraction of combinatorial background;
• application of neural networks to particle identification and tracking, signal reconstruction, and prediction problems;
• use of GEANT4 for the simulation of detectors and experimental setups;
• coincidence measurements between detectors at different distances, including the use of GPS-based synchronization;
• digitization and analysis of signals from detectors;
• characterization of FET-based electronic systems.

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

2) C.M.Bishop, Neural networks and their applications, Rev.of Sci.Instr.65(1994)1803

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

4) M.Momayezi et al., Applications of real-time digital pulse processing in nuclear physics, AIP Conference Proceedings 518, 307 (2000); https://doi.org/10.1063/1.1306025

5) Further specific references provided during the lectures.

At the end of the course, each student has to write a report (in the format of a scientific paper or presentation or laboratory report) about one of the experimental activities carried out during the course. The students will be questioned about the report they produced and about the other contents of the course.

The final evaluation will take into account the following aspects:

• knowledge of the contents
• clarity and language skills
• relevance of the answers to the asked questions
• ability to make correct links with other topics in the program
• ability to report examples
• ability to solve simple exercises and make estimates

Learning assessment may also be carried out on-line, should the conditions require it. 

To ensure equal opportunities and in compliance with current laws, interested students may request a personal interview in order to plan any compensatory and/or dispensatory measures based on educational objectives and specific needs. Students can also contact the CInAP (Centro per l’integrazione Attiva e Partecipata — Servizi per le Disabilità e/o i DSA) referring teacher within their department (https://www.cinap.unict.it/content/referenti).

Discuss one advanced gas detector.

Discuss one advanced solid state detector.

Describe the main characteristics of a digitizer.

Discuss one technique for the combinatorial background estimation.

Discuss an application of ANN in phyiscs.

Describe the use of event generator in nuclear physics.