Academic Year 2021/2022 - 1° Year - Curriculum NUCLEAR AND PARTICLE PHYSICS
Teaching Staff: Agatino MUSUMARRA
Credit Value: 6
Scientific field: FIS/01 - Experimental physics
Taught classes: 21 hours
Exercise: 45 hours
Term / Semester:

Learning Objectives

Advanced experimental approach - Innovation capability.

Complementing knowledge concerning various methods of charged and neutral particle detection by the description of state-of-the-art devices, special insight to solid-state detectors.

Methodologies for advanced data analysis: advanced multi-parametric statistical analysis (PCA, SVD, cluster analysis, soft-computing, machine learning). Proposing an alternative point of view to data analysis for Nuclear Physics experiments, generalizing the skill to different statistical environments and data classes.

Developing independent and original analytical skills by approaching state-of-the-art instrumentation for Nuclear Physics experiments.

Final goals: Design, implementation and validatation of an innovative detection system. Capability to perform a data analysis according to the peculiar features of the data set. Writing and editing a scientific-technical paper, suitable for journal submission.

"Dublin Descriptors": the course allows to acquire the following transversal skills:

Knowledge and understanding:

- Perform modelization of a physical system, validation by an experiment.

- Implementation of a detection system by using the appropriate relationships between physical quantities and instrumentation.

- Skills in designing and developing an experimental setup for measurements to be carried out in the field of Nuclear Physics.

- Ability to perform advanced data analysis obtained by multiparametric experimental equipment.

- Design and build of an experimental apparatus for charged and neutral particle detection.

- Ability to perform numerical modelling of a device.

Ability to apply knowledge:

- Capability to apply the acquired knowledge for describing physical phenomena by using the scientific method.

- Ability to develop new models of electronic devices.

- Ability to perform advanced numerical simulations based on the physics underlying the detection system.

- Skills in designing advanced physics experiments and performing related data analysis.

Autonomy of judgment:

- Critical and innovative reasoning.

- Ability to identify appropriate methods for data analysis, understanding and processing experimental data.

- Capability of prediction by a model describing a detection device.

- Skills in validating sensors specifications and associated electronics, response and calibration.

Course Structure

6 CFU:

- Theory (21 hours),

- Laboratory sessions on state-of-the-art devices, implementation of advanced data analysis techniques and device characterization,

functional validation with respect to the expected specifications (45 hours).

In case of remote learning, changes with respect to the planned program may be introduced.

Detailed Course Content

Theory: review on specific topics of radiation matter interaction : Photons, heavy charged-particles and neutrons.

Physics and structure of solid state devices: from Si-junction to CMOS devices.

Signal formation in a solid state devices, Ramo's theorem.

Interaction of neutrons with matter, converters and standards. Advanced solid state detectors.

Silicon detectors for specific applications and segmented detectors for photons, charged-particles and neutrons: Monolithic detectors, Micro and Macrostrip detectors, Silicon drift detectors, SiPM, CCD and CMOS).

Neutron detectors: converter-detector scheme, detectors for slow and fast neutrons, neutron beam imaging, detectors for neutron-flux measurement: application in Nuclear Physics.

Neutron imaging and neutron tracking: CMOS devices: use and potential in Nuclear Physics.

Front-end electronics and DAQs: techniques in signal digitizers: bandwidth, sampling rate, real-time data flow processing, online data flow and data reduction.

Acquisition systems with and without triggers with examples.

Table-top and integrated digitizing modules. Technical specs, use in the field of a real multi-parametric data acquisition system, tuning specifications according to the input signals.

Advanced off-line analysis techniques. Multi-parametric analysis through rotation of the the data matrix: PCA and SVD, interpretation of the Biplots as a function of the input data, generalization of the data structure.

Cluster analysis, basic concepts and algorithms, metrics and examples.

Machine learning: unsupervised learning. Applications of neural networks in Nuclear Physics, image recognition; examples and applications in Nuclear Physics and extension to general applications and consumer devices.

Lab activities: MATLAB Analysis Tool: MATLAB: introduction and philosophy according to the concepts transmitted by the course. Data representation. Signal analysis: integration, differentiation, filter functions, smoothing functions, noise reduction, signal parameter determination.

Advanced multi-parametric statistical analysis through PCA and Cluster Analysis. Introduction to computational parallelization (hardware, libraries and CUDA functions).

Graphical representation, comparison with the "R" analysis tool. Implementation of detection systems involving advanced solid state devices: characterization of SiPM and CMOS devices in applications for gamma, neutron and X spectrometry (XRF and DXRF).

Implementation of a neutron detection system. Gamma-n discrimination via PSD through the use of MATLAB.

Setup of a tracking device for charged particles / neutrons.

Validation of the laboratory experiments by measurements, evaluation of the data coherence by post-analysis representation.

Writing of a scientific paper according to the criteria for submission to a scientific journal.

Textbook Information

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

Claus Grupen, Boris Shwartz Particle Detectors, Cambridge University Press

S.M SZE,Semiconductor Devices: Physics and Technology, WILEY

I.T. Jolliffe, Principal Component Analysis, Springer Series in Statistics

K. Gurney, An Introduction to Neural Networks, Taylor & Francis Ltd