Speaker
Prof.
Stephen Watts
(Brunel University)
Description
Visualisation of data in particle physics currently involves event displays,
histograms and scatterplots. Since 1975 there has been an explosion of techniques for
data visualisation driven by highly interactive computer systems and ideas from
statistical graphics. This field has been driven by demands for data mining of large
databases and genomics. Two key areas are direct manipulation of visual data, and new
methods for visualising high-dimensional data. The first area has seen the use of
linked views, brushing and pruning. The second area has seen the introduction of
methods such as parallel coordinates and the grand tour.
In this paper, these ideas are applied to particle physics data to evaluate their
ability to reduce data analysis time and improve pattern recognition. In particular,
parallel coordinates will be used to analyse a sample of K-short Monte Carlo events.
It will be shown that this graphical technique significantly reduces the time taken
to determine the key variables for event selection.
This paper will also evaluate various publicly available software tools that include
many of the new statistical graphics techniques. The paper will conclude that no
single tool includes all the most powerful new techniques and argue that urgent work
is required to integrate these ideas into data analysis tools for particle physics.
Primary author
Prof.
Stephen Watts
(Brunel University)
