Statistical Learning in High Energy and Astrophysics

This thesis studies the performance of statistical learning methods
in high energy and astrophysics where they have become a standard
tool in physics analysis. They are used to perform complex
classification or regression by intelligent pattern recognition.
This kind of artificial intelligence is achieved by the principle
``learning from examples'': The examples describe the relationship
between detector events and their classification. The application
of statistical learning methods is either motivated by the lack of
knowledge about this relationship or by tight time restrictions. In
the first case learning from examples is the only possibility since
no theory is available which would allow to build an algorithm in
the classical way. In the second case a classical algorithm exists
but is too slow to cope with the time restrictions. It is therefore
replaced by a pattern recognition machine which implements a fast
statistical learning method. But even in applications where some
kind of classical algorithm had done a good job, statistical
learning methods convinced by their remarkable performance. This
thesis gives an introduction to statistical learning methods and
how they are applied correctly in physics analysis. Their
flexibility and high performance will be discussed by showing
intriguing results from high energy and astrophysics. These include
the development of highly efficient triggers, powerful purification
of event samples and exact reconstruction of hidden event
parameters. The presented studies also show typical problems in the
application of statistical learning methods. They should be only
second choice in all cases where an algorithm based on prior
knowledge exists. Some examples in physics analyses are found where
these methods are not used in the right way leading either to wrong
predictions or bad performance. Physicists also often hesitate to
profit from these methods because they fear that statistical
learning methods cannot be controlled in a physically correct way.
Besides there are many different statistical learning methods to
choose from and all the different methods have their advantages and
disadvantages -- compared to each other and to classical
algorithms. By discussing several examples from high energy and
astrophysics experiments the principles, advantages and weaknesses
of all popular statistical learning methods will be analysed. A
focus will be put on neural networks as they form some kind of
standard among different learning methods in physics analysis.