Course Unit Page

Academic Year 2021/2022

Learning outcomes

At the end of the course the student will have a basic knowledge of modern electronic data collection systems and advanced knowledge in the field of modern computer systems for experimental data processing and Monte Carlo simulation. In particular, the student will be able to: sketch the selection criteria related to the "online" data flow and to the "offline" processing, including event reconstruction, detector calibration and data analysis.

Course contents

Unit I :

Introduction to the therminlogy and basic concepts of a Data Acquisition system. Basic concepts of "trigger" for the online data selection. Multi-level triggers, both hardware and software (HLT).  Examples of trigger systems currently used in the experiments at the LHC. 


Unit II:

Introduction to the reconstruction and analysis of physics events. Global and local methods of pattern recognition- Track Finding e Track Fitting- determination of the track parameters – Kalman Filter. Algorythms for Particle Identification (Bayesian PID).
General concepts on the calibration and alignment of the detectors, with examples of the techniques used in the LHC experiments.


slides and reviews on the topics presented during lectures

Teaching methods

Lectures and laboratory sessions

Assessment methods

Oral examination on the topics presented during the course.

Teaching tools

Slides and extra material that will be  available on Insegnamenti Online. Laboratory sessions on Data Aquisition and Techniques of track reconstraction (coding of a kalman filter using  ROOT)

Office hours

See the website of Silvia Arcelli

See the website of Pietro Antonioli