PHYSICS LABORATORY III

Module FRONTAL TEACHING

The learning objectives of the course are the following:

• Provide theoretical and practical knowledge of experimental techniques concerning the interaction of radiation with matter, particle detectors, signal processing, electronics as well as statistical methods for simulation and data analysis.
• Make the students able to perform measurements using appropriate instrumentation.
• Provide basic knowledge on laboratory instrumentation.
  
• Make the students able to design simple experimental setups.
  
• Make the students able to perform simple estimations and produce graphs to analyse experimental data.
  
• Provide basic knowledge on simulation techniques and Monte Carlo method.
  
• Introduce students to the Python data analysis framework.
  
• Improve students' skills (writing and speaking), to describe the topic, the methods, the results, the analysis procedure and the interpretation of the experiments output.
  

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

Knowledge and understanding:

• Ability in induction and deduction methods.
• Ability to schematize a real problem, defining the physical quantities (scalars and vectors) that are essential for obtaining results.
  
• Capability to setup and define a problem by using quantitative relations (algebraic, differential integral) between physical variables and to solve it by means of analytical or numerical algorithms.
  
• Capability to design simple experimental setups or to use scientific instrumentation to perform thermo-mechanics and electromagnetic measurements.
  
• Capability to carry out statistical analyses of results.
  
• Capability to perform analysis sessions of experimental data from modern physics experiments.
  
• Capability to perform numerical simulations.

Capability to apply the knowledge in order to:

• Describe physical phenomena by a correct and quantitative application of scientific methodologies.
• Capability to develop theoretical models.
  
• 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:

• Abilities in computer programming.
  
• 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.

Learning skills:

• Develop autonomy in designing and carrying out experimental activities and in preparing a scientific report.

The course includes lectures, practical laboratory activities, and computer-based exercises for data analysis and simulation. More specifically, 6 CFU (corresponding to 7 hours per CFU) are devoted to lectures, for a total of 42 hours, while 3 CFU (corresponding to 15 hours per CFU) are devoted to laboratory and practical exercises, for a total of 45 hours. The 9-CFU course therefore comprises a total of 87 hours of teaching activities.

• The lectures provide the theoretical knowledge related to laboratory techniques, radiation–matter interactions, particle detectors, signal processing, associated electronics, and statistical, numerical, and Monte Carlo methods. This knowledge is subsequently applied during the practical activities, promoting the transition from the understanding of theoretical concepts to their experimental application.
• The laboratory activities enable students to become familiar with the instrumentation, perform measurements, design simple experiments, and critically assess the experimental procedures, the reliability of the measurements, and the results obtained. Computer-based exercises develop students’ skills in numerical and graphical data analysis, simulation, and the use of the Python framework. Laboratory activities are also carried out in groups and promote the development of the ability to collaborate, discuss experimental choices, critically analyse and interpret results, and identify potential issues.
• Finally, the preparation of reports on the practical exercises and on one of the experiments carried out in the laboratory progressively develops students’ ability to clearly and systematically describe the problem addressed, the methodologies adopted, the results obtained, and their interpretation. These skills are further consolidated through the final oral examination.

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.

• It is necessary to have basic knowledge about general Physics, modern Physics, mathematical analysis, the error theory in an experimental measurement and data analysis techniques. For this reason, as required by the Didactic Regulations, it is mandoratory to have passed the following exams: Mathematical Analysis I, General Physics I and II, Laboratory of Physics I and II;
• It is also useful to have basic knowledge of  condensated matter Physics and nuclear Physics.

Attendance in the laboratory, however, is mandatory (> 75%) as the experimental skills required by the course learning objectives involve direct interaction with the instrumentation, performing measurements, and addressing the practical issues that arise during the experiments. 

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.

Part I

1. Techniques and laboratory instrumentation

Sensors for the measurement of physical quantities - Analog and digital sensors - Data acquisition from sensors - Digital multimetere- Analog and digital oscilloscopes - Vacuum techniques - Elements for vacumm production and measurement - Measurement of radiations from Infrared to ultraviolet - Optical fibers - Spectrophotometers - Radioactive sources

2. Radiation Detectors

Interaction of charged particle with matter - Bethe-Block relation - Range - Straggling - Energy loss of electrons and positrons - Photon interaction - Photoelectric effect - Compton Effect - Pair production - Electromagnetic showers - Particle detectors - Measure of energy, momentum, position, mass and charge of particles - General properties of a detector: sensitivity, resolution, efficiency, dead time - Gas detectors - Ionization chambers - Geiger counters - Solid state detectors - Strip, drift and pixel detectors - Radiation damage - Scintillation detectors - Light yield - Photomultipliers - Light guides and WLS fibers - APD, silicon photomultiplier, CCD, acquisition and reduction of stellar photometric and spectroscopic images.

3. Elements of Electronics

Pulse signals from detectors - Analog and digital signals - Propagation of signals - Coaxial cables - SIgnal Generators- Power supply - Electronics for Nuclear Physics - The NIM standard - Linear electronics: preamplifier, amplifier, shapers - Basic knowledge of logic electronics: OR, AND, NOT circuits - Analog-to-digital converters (ADC and QDC) - Discriminators - Coincidence circuits - Counters - Trigger systems - Data acquisition - Digital pulse processing

4. Data analysis and simulation techniques

Knowledge of elementary statistics - Central values and dispersion indexes - Experimental distributions - Gauss and Poisson distributions - Experimental errors - SIgnificance test - Data analysis techniques in nuclear physics experiments - Spectra analysis - Background subtraction - Non linear fits . Multiparametric analysis - The Python language - SImulation of physical processed - Monte Carlo methods the GEANT package for detector simulation

Part II: Laboratory experiments

• Exercises on the use of data logger, sensors and Arduino Board
• Exercises on the use of laboratory instrumentation (multimeter, oscilloscope, electronics,..)
• Exercises on the use of the Python language
• Laboratory experiments (11 in total, listed below, randomly assigned to students for the prepartion of a written report to be discussed during the oral exam)

1. Photoelectric effect and the measurement of the Planck constant
2. Study of discrete and continuous light spectra with a digital spectrophotometer
3. Detection of electrons with a Geiger counter and study of the absorption coefficient
4. Study of the light absorption at different frequencies
5. Gamma spectrometry and absorption coefficient with scintillators
6. Alpha spectrometry and study of energy loss with silicon detectors
7. Measurement of the energy spectrum of a beta source
8. Michelson interferometer
9. Leslie cube
10. Zeeman effect
11. Stellar Photometry and Spectroscopy observation at the Catania Observatory

For the items concerning the interaction of particle and radiation with matter, particle detectors and electronics see one of the following textbooks:

1. William R. Leo, Techniques for Nuclear and Particle Physics Experiments, Springer-Verlag

2. Glenn F. Knoll, Radiation Detection and Measurement, John Wiley and Sons

3. Claude Leroy and Pier-Giorgio Rancoita, Principles of Radiation Interaction in Matter and Detection, World Scientific

4. C.Grupen, B.Schwartz, Particle Detectors, Cambridge

For items concerning statistics and data analysis techniques:

5. J.R.Taylor, Introduzione all’analisi degli errori, Zanichelli

For Arduino:

6. B.W. Evans, Arduino Programming Notebook, Creative Commons

All the presentations shown during the lessons and additional material (manuals, papers, codes, etc etc) are provided during the course.

Assessment procedures require:

• Attendance of at least 75% of the laboratory sessions.
• Submission of a detailed report on one of the laboratory experiments eligible for selection (the complete list is provided in the Course Contents section). The experiment assigned for the report is randomly assigned and communicated at the end of the course. The report must be submitted one week before the oral examination, following the instructions provided by the professors during the course.
• Submission of four short reports on selected laboratory exercises carried out during the course (verification of the Poisson distribution using a Geiger counter, use of Arduino, use of a digital multimeter, application of the Bethe–Bloch formula, and estimation of the geometric acceptance through Monte Carlo simulations). These reports must be submitted together with the detailed report, according to the instructions provided by the professors during the course.
• An oral examination covering the detailed report, the short reports, and the other topics addressed during 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.

As required by the Didactic Regulations, it is mandoratory to have passed the following exams: Mathematical Analysis I, General Physics I and II, Laboratory of Physics I and II.

The following list of questions is not exhaustive but includes just some examples.

Charged particles interaction with matter and energy loss - Gamma interaction with matter - Working principle of a gas detector - Scintillation detectors - Properties of a scintillator - Energy resolution of a detector - Time resolution of a detector - Estimation of the geometrical acceptance of a detector - Calibration of a detector - Analog to digital converter - Discriminators - Coincidence circuit and spurious coincidences rate - Examples of Monte Carlo simulations.