Concepts for Future Hadron Collider Experiments 2

In the second part of the lecture "Concepts for Experiments at Future Hadron Colliders" the following topics are covered:

I. Statistical methods for data analysis:

• methods of point estimation
• methods of interval estimation
• tests of simple and composed hypotheses

II. Methods of particle identification:

• detection of charged particle, method of track reconstruction and momentum determination
• electron and muon identification
• jet algorithms

III. Basic principles of the electronics for the read-out of particle detectors:

• signal types: analog and digital signals
• basic circuits for the processing of analog signals
• basic digital circuits

IV. Basics principle of modern trigger systems:

• conventional hardware triggers
• modern hardware triggers as a synthesis of hardware and software triggers

After successful completion of the module the students are able to:

• perform statistical analysis of experimental data
• develop and apply algorithms for event reconstruction and particle identification
• design simple read-out electronics circuits
• develop triggering concepts

No preconditions in addition to the requirements for the Master’s program in Physics.

The lecture covers four major topics which build upon each other. Each topic is introduced with intuitive examples and then worked out step by step. At the end of each topic the learned methods are applied to relevant problems of present-day experimental particle physics. On request by the students illustrating computer programmes will be written during the lecture in cooperation with the students.

• blackboard
• lecture notes
• supplementary Powerpoint presentations

• F. James: Statistical Methods in Experimental Physics, WSPC, (2006)
• I. Brock & Th. Schörner-Sadenius: Physics at the Terascale, Wiley-VCH, (2011)
• W.R. Leo: Techniques for Nuclear and Particle Physics Experiments, Springer, (1994)

There will be an oral exam of 25 minutes duration. Therein the achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using comprehension questions and sample calculations.

For example an assignment in the exam might be:

• What is a simple hypothesis?
• What is a likelihood function?
• Give a simplified description of an algorithm for the reconstruction of charged particle tracks!
• What signal types do you know?
• Describe a simple electronics circuit for an inverting amplifier!

In the exam no learning aids are permitted.