Gilles Maurin

Pierre Aubert, Thomas Vuillaume, Gilles Maurin et al.

The new generation research experiments will introduce huge data surge to a continuously increasing data production by current experiments. This data surge necessitates efficient compression techniques. These compression techniques must guarantee an optimum tradeoff between compression rate and the corresponding compression /decompression speed ratio without affecting the data integrity. This work presents a lossless compression algorithm to compress physics data generated by Astronomy, Astrophysics and Particle Physics experiments. The developed algorithms have been tuned and tested on a real use case~: the next generation ground-based high-energy gamma ray observatory, Cherenkov Telescope Array (CTA), requiring important compression performance. Stand-alone, the proposed compression method is very fast and reasonably efficient. Alternatively, applied as pre-compression algorithm, it can accelerate common methods like LZMA, keeping close performance.

Mikaël Jacquemont, Thomas Vuillaume, Alexandre Benoit et al.

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper, we present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data. We show that it outperforms the standard analysis, both on simulated and on real data, thus validating the deep approach for the CTA data analysis. This work also illustrates the difficulty of moving from simulated data to actual data.