2007 2008 Academic Training programme

LECTURE SERIES

27, 28 & 29 May 2008

11:00 -12:00 – Main Auditorium, bldg. 500-1

The biological effects of ionizing radiation

M. STREIT-BIANCHI, CERN, Geneva, CH

Since the discovery of X-rays the practical use of ionizing radiation and its damaging effects have been a source of concern for occupational health and radiation protection. This led to the introduction of dose limits and strict controls associated with the use of radiation for civil uses. This Academic Training lecture series will discuss the effects of radiation on humans with special emphasis on the health effects of low doses. Radiation risks as assessed from Hiroshima and Nagasaki, Chernobyl as well as others accidental and occupational exposures will be presented and discussed.

2, 3, 4, 5, 6 June 2008

11:00 -12:00 – Main Auditorium, bldg. 500-1

Technology and applications of high field accelerator magnets

Dr. G. AMBROSIO, Fermi National Accelerator Laboratory, USA

Superconducting magnets are an enabling technology for high energy hadron colliders. High field superconducting magnets are needed to increase beam energy and luminosity beyond the limits of present colliders (operational or close to operation).

In this lecture series we will look at needs and options for high field accelerator magnets from the point of view of: conductors, coil fabrication technologies, magnetic and mechanical designs, magnet assembly, quench protection and lifetime issues. Possible applications will be presented as case studies.

16, 17, 18, 19 June 2008

11:00 -12:00 – Main Auditorium, bldg. 500-1

Multivariate statistical methods and data mining in particle physics

Dr. Glen COWAN, London University, Royal Holloway College, UK

The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.