EXPERIMENTAL METHODS FOR NUCLEAR PHYSICS

Learn advanced detection techniques and the main experimental methods in data analysis for nuclear physics experiments.

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

Knowledge and understanding:

• 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 physical variables and to solve it by means of statistical 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.

The course corresponds to 6 CFU, for a total of 21 h of theory and 45 h of experimental activities (training and analysis sessions, laboratory activities).

The following teaching methods will be used during the course:

1) Lectures

2) Numerical exercises

3) Data analysis sessions

4) Laboratory activities

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.

Introductory courses in Nuclear Physics

Knowledge of statistics and experimental data processing

Basic computer skills

Knowledge of the ROOT analysis framework

Attendance at the course is normally compulsory (consult the Teaching Regulations of the Course of Studies).

Advanced detection techniques:

Advanced Detectors: review on solid state and gas detectors, examples of advanced solid state and gas detectors (CCD, strip detectors, MAPS and hybrid pixels, CMOS, LGAD, wide bandgap detectors, TPC, GEM, MRPC), electromagnetic calorimeter, advanced applications of scintillators, Cherenkov detectors, RICH.

Trigger systems, data acquisition and transmission: multiparametric data acquisition systems, trigger design and event selection, trigger-less readout systems, artificial intelligence in on‑line data processing

Digital pulse processing: review on 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.

Data analysis techniques:

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.

Tracking and pattern recognition methods: pattern recognition methods, Hough transform and its application to RICH detectors, tacking methods, track recognition and reconstruction, primary and secondary vertex finding, Kalman Filter method, shower analysis for calorimeters, shape analysis.

Neural network methods: Artificial neural networks (ANN), applications and examples in nuclear physics (particle identification, particle tracking, signal reconstruction, forecast methods, use of neural network algorithms for classification).

Monte Carlo methods and detector simulation: review on basic of Monte Carlo methods, simulation techniques for the evaluation of detector properties, professional simulation tools in nuclear physics and related areas.

Analysis sessions and laboratory activities:

Analysis 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, tansverse momentum 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 (anti), Armenteros plot.

GEANT: use and applications of the GEANT software.

Experimental activities: coincidences between far and close detectors, digitalization and analysis of signals from detectors, characterization of MAPS sensors.

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) 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

The verification of learning will be done remotely if the circumstances would require online or blended teaching.

To guarantee equal opportunities and in compliance with the laws in force, interested students can ask for a personal interview in order to plan any compensatory and / or dispensatory measures, based on the didactic objectives and specific needs. It is also possible to contact the referent teacher CInAP (Center for Active and Participated Integration - Services for Disabilities and / or SLD) of our Department, Prof. Catia Petta.

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.