International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Deep Learning for Astroparticle Physics

1 Jun 2022, 15:20

Catania

Conveners

Deep Learning for Astroparticle Physics

• Francesco Schilliro' (Istituto Nazionale di Astrofisica (INAF))

35. ML Event Reconstructions for Neutrino Telescopes

Philipp Eller

01/06/2022, 15:20

Oral Presentation

Neutrino telescopes, such as IceCube, KM3NeT or GVD, consist of thousands of photo sensors distributed over cubic kilometer volumes. The Cherenkov light from particle interactions within create signals of varying shapes and sparsity. For the reconstruction of such physics events, one aims to infer quantities like the interaction vertex, the deposited energy, the angles, and the topology of the...

36. Gamma/hadron separation for the MAGIC Imaging Atmospheric Cherenkov Telescopes through Convolutional Neural Networks trained with real data samples

Stefano Truzzi (University of Siena/INFN Pisa)

01/06/2022, 15:40

Oral Presentation

Imaging Atmospheric Cherenkov Telescopes (IACT) are able to indirectly detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic objects known, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by IACTs through imaging, the Cherenkov light produced by the charged superluminal...

140. Gamma/hadron separation for the MAGIC Imaging Atmospheric Cherenkov Telescopes through Convolutional Neural Networks trained with real data samples

Mr Stefano Truzzi (University of Siena / I.N.F.N. Pisa, Italy)

Oral Presentation

Abstract: Imaging Atmospheric Cherenkov Telescopes (IACT) are able to indirectly detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic objects known, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by IACTs through imaging, the Cherenkov light produced by the charged...

82. ML Event Reconstructions for Neutrino Telescopes

Dr Philipp Eller (TUM)

Oral Presentation

Neutrino telescopes, such as IceCube, KM3NeT or GVD, consist of thousands of photo sensors distributed over cubic kilometer volumes. The Cherenkov light from particle interactions within create signals of varying shapes and sparsity. For the reconstruction of such physics events, one aims to infer quantities like the interaction vertex, the deposited energy, the angles, and the topology of...