In gamma-ray astronomy, the analysis of the images produced by Cherenkov telescopes consists in separating the gamma events from the background (cosmic rays), and reconstructing the parameters of the detected gammas. The first step is complex because cosmic rays can generate very similar images and the signal-to-noise ratio is typically lower than 1/1000. Besides, CTA, the next generation of observatories, will improve the sensitivity by an order of magnitude, with the counterpart of generating PB of data each year. As a result, standard analysis methods either are too slow or lack sensitivity at low energies.
In this talk, we present a deep multitask architecture, named γ-PhysNet, that outperforms a standard method relying on the Hillas parameter extraction and a multivariate method. Specifically, it achieves very interesting sensitivity below 200 GeV, and could enhance the study of transient phenomena. γ-PhysNet is also 800 times faster than the state-of-the-art method. While analysis models are trained on simulated data, we also show that γ-PhysNet obtains better results on the first exploitable data provided by the Large-Sized Telescope prototype of CTA. Finally, we present first steps towards the explainability of the model’s decisions.
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