Deep Learning based Algorithms in Astroparticle Physics

Journal of Physics Conference Series, vol. 1525, pp. 012112

Abstract

Abstract In recent years, great progress has been made in the fields of machine translation, image classification and speech recognition by using deep neural networks and associated techniques (deep learning). Recently, the astroparticle physics community successfully adapted supervised learning algorithms for a wide range of tasks including background rejection, object reconstruction, track segmentation and the denoising of signals. Additionally, the first approaches towards fast simulations and simulation refinement indicate the huge potential of unsupervised learning for astroparticle physics. We summarize the latest results, discuss the algorithms and challenges and further illustrate the opportunities for the astrophysics community offered by deep learning based algorithms.

Authors 2

1. Martin Erdmann Aachen III. Physikalisches Institut A

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, III. Physikalisches Institut A, Otto-Blumenthal-Str., 52056 Aachen, Germany

2. Jonas Glombitza corresponding Aachen III. Physikalisches Institut A

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, III. Physikalisches Institut A, Otto-Blumenthal-Str., 52056 Aachen, Germany