Speaker
Ms
Sonia Khatchadourian
(ETIS - UMR CNRS 8051)
Description
The HESS project is a major international experiment currently performed
in gamma astronomy. This project relies on a system of four Cherenkov
telescopes enabling the observation of cosmic gamma rays. The
outstanding performance obtained so far in the HESS experiment has led
the research labs involved in this project to improve the existing
system: an additional telescope is currently being built and will soon
take place within the previous telescope system. This telescope is
designed to be more sensitive to the detection of low energy particles
than the others, leading to an increase of the number of collected
particle images. In this context which is tightly constrained in terms
of latency, physicists have been compelled to design an additional L2
Trigger in order to deal with a huge amount of data. This trigger aims
at selecting images of interest (ie. gamma particles) and rejecting all
other events that are associated to noise. Contrary to classical methods
that consist of strong cuts based on Hillas parameters, we propose an
original approach based on artificial neural networks.
In this approach, collected events are first handled by a pre-processing level whose purpose consists in applying transformations on incoming images, thus reducing the dimensionality of the problem. It is based on Zernike moments computation that aims to extract the main features of the images and guarantee image invariance in translation and rotation. Zernike moments have also proved to be reliable in terms of their feature representation capability and low noise sensitivity.
In a second step, an artificial neural networks ensures the classification of events within two classes (gammas and hadrons), indicating whether to keep the image for future processing or to reject it.
In this presentation, we will describe the entire L2-Trigger system and provide some results in terms of classification performances. We will discuss the contribution of neural networks in this type of experiments compare to classical solutions.
Author
Ms
Sonia Khatchadourian
(ETIS - UMR CNRS 8051)
Co-authors
Mr
Jean-Christophe Prévotet
(IETR - UMR CNRS 6164)
Mr
Lounis Kessal
(ETIS - UMR CNRS 8051)
