Computational Laboratory for Particles, Astroparticles, and Fundamental Interactions
A.Y. 2025/2026
Learning objectives
The course provides an introduction to the tools used for signal extraction from the background and the study of systematic uncertainties for parameter determination in particle and astroparticle physics experiments. It also covers the subsequent comparison with phenomenological models through the calculation of discovery and exclusion limits, using concrete examples related to current and future experiments.
Expected learning outcomes
At the end of the course, the student will:
- be able to use simulation tools for the study of fundamental interactions,
- understand techniques for identifying particles reconstructed in simulated events,
- be capable of planning an analysis for signal extraction from the background, with particular attention to the issues and systematic uncertainties related to particle reconstruction and identification,
- be familiar with statistical and data analysis techniques to identify signal events, even those that are sub-dominant compared to the background, or to set upper limits in the case of non-identification,
- know examples of data analysis in current particle and astroparticle physics experiments,
- be able to recognize critical aspects in the design of future experiments.
- be able to use simulation tools for the study of fundamental interactions,
- understand techniques for identifying particles reconstructed in simulated events,
- be capable of planning an analysis for signal extraction from the background, with particular attention to the issues and systematic uncertainties related to particle reconstruction and identification,
- be familiar with statistical and data analysis techniques to identify signal events, even those that are sub-dominant compared to the background, or to set upper limits in the case of non-identification,
- know examples of data analysis in current particle and astroparticle physics experiments,
- be able to recognize critical aspects in the design of future experiments.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Module 1 - Monte Carlo Generators
Automatic generation of scattering amplitudes through Feynman diagram calculations. Basics of multidimensional numerical integration. Adaptive and multi-channel integration. Event generation and the difference between weighted and unweighted events. Simulation of scattering processes relevant to collider experiments. Fundamentals of parton showering. Use of MadGraph and Pythia software through specific exercises.
Module 2 - From Event Simulation to Object Reconstruction and Identification
Simulation of detector response using fast simulation packages. Structure and use of detector cards in Delphes. Reconstruction of tracks, electrons, muons, jets, and missing transverse energy. Reconstruction efficiencies and fake rates. Comparison between generator-level and reconstructed objects. Analysis of kinematic distributions and the effects of detector resolutions. Use of Delphes software through specific exercises.
Module 3 - Statistics
Introduction to the concepts of p-value and chance coincidence probability. Statistical significance, null hypothesis, confidence intervals, and the calculation of upper/lower limits and exclusion plots.
Module 4 - HEP Analysis
Design of a complete analysis in high-energy physics at the Large Hadron Collider using real ATLAS experiment data. Signal selection and background rejection through the definition of analysis regions (signal, control, and validation regions). Study of kinematic distributions and construction of discriminating variables to maximize sensitivity. Estimation of background contributions.
Module 5 - Astroparticle Analysis
Complete analysis from detector level to astrophysical conclusions using real data from the Pierre Auger Observatory on ultra-high-energy cosmic rays. Examples of data analysis strategies for the search for rare events (solar neutrinos, dark matter, paleo-detectors).
Automatic generation of scattering amplitudes through Feynman diagram calculations. Basics of multidimensional numerical integration. Adaptive and multi-channel integration. Event generation and the difference between weighted and unweighted events. Simulation of scattering processes relevant to collider experiments. Fundamentals of parton showering. Use of MadGraph and Pythia software through specific exercises.
Module 2 - From Event Simulation to Object Reconstruction and Identification
Simulation of detector response using fast simulation packages. Structure and use of detector cards in Delphes. Reconstruction of tracks, electrons, muons, jets, and missing transverse energy. Reconstruction efficiencies and fake rates. Comparison between generator-level and reconstructed objects. Analysis of kinematic distributions and the effects of detector resolutions. Use of Delphes software through specific exercises.
Module 3 - Statistics
Introduction to the concepts of p-value and chance coincidence probability. Statistical significance, null hypothesis, confidence intervals, and the calculation of upper/lower limits and exclusion plots.
Module 4 - HEP Analysis
Design of a complete analysis in high-energy physics at the Large Hadron Collider using real ATLAS experiment data. Signal selection and background rejection through the definition of analysis regions (signal, control, and validation regions). Study of kinematic distributions and construction of discriminating variables to maximize sensitivity. Estimation of background contributions.
Module 5 - Astroparticle Analysis
Complete analysis from detector level to astrophysical conclusions using real data from the Pierre Auger Observatory on ultra-high-energy cosmic rays. Examples of data analysis strategies for the search for rare events (solar neutrinos, dark matter, paleo-detectors).
Prerequisites for admission
* Fundamentals of particle and astroparticle physics (basic knowledge of the Standard Model).
* Basic knowledge of statistics and probability.
* Basic programming skills.
* Basic knowledge of statistics and probability.
* Basic programming skills.
Teaching methods
The course consists of lectures and computational laboratory sessions.
Attendance is mandatory.
Attendance is mandatory.
Teaching Resources
Lecture slides and other material that will be distributed during the lectures and will be available on the course's ARIEL website.
Assessment methods and Criteria
The assessment is graded on a 30-point scale and consists of an independent practical test, followed by an oral examination designed to assess methodological understanding and the ability to carry out experimental analyses autonomously.
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS - University credits: 6
Laboratories: 48 hours
Lessons: 14 hours
Lessons: 14 hours
Professor(s)