The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray telescopes for gamma-ray astronomy. Two arrays will be deployed composed of 19 telescopes in the Northern hemisphere and 99 telescopes in the Southern hemisphere. Due to its very high sensitivity, CTA will record a colossal amount of data that represent a computing challenge to the reconstruction software. Moreover, the vast majority of triggered events come from protons that represent a background for gamma-ray astronomy. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data in many domains. Thanks to the huge amount of simulated data and later of real data, produced by CTA, these algorithms look well-suited and very promising. Moreover, the trained neural networks show very good computing performances during execution.
The aim of the GammaLearn project is to find the best possible neural networks for gamma / cosmic rays separation and gamma parameters reconstruction. As the Deep Learning is a very empirical process, many hyperparameters and parameters initialization combinations will be explored and hopefully fine tuned. This represents a lot of learning cycles, and highlights the need of a tool to ease this process.
The GammaLearner framework has been designed to address this issue, automatically dealing with bookkeeping all the experiments information. Moreover, it enables the use of the indexed operations and stereoscopy introduced before and developed especially for CTA in a
more friendly manner.
This work has been presented at the CHEP 2018 conference and published in its proceedings, Deep Learning applied to the Cherenkov Telescope Array data analysis, M. Jacquemont, T. Vuillaume, A. Benoit, G. Maurin, P. Lambert, G. Lamanna, CHEP 2018 Conference