Speaker
Description
Imaging atmospheric cherenkov telescopes are equipped with ultra high speed and single photon sensitive camera. Any extensive air shower will leave a signal of short duration (~10-20 ns) in the camera which contains the information about the primary particle type, energy and direction.
Since many years, this signal is transformed into an image from which a spatial parametrisation (Hillas parameters) is performed. These parameters are fed to boosted decision trees and more recently random forests to be able to discriminate gammas from protons, reconstruct their energies and eventually their directions.
The advent of both convolutional neural networks for image recognition and powerful computing ressources open a new era in gamma ray analysis. The particle type, energy and direction are now inferred from calibrated images without prior spatial parametrisation and provide, on simulation data, much better results than traditional analysis.
Based on this observation, the multi-messenger astronomy group at DPNC has developed several analysis based either on auto-encoders for data volume reduction, 1D CNN for photon stream extraction and more recently anomaly detection an CNN for trigger purposes. These works will be presented in the proposed poster.
