Deep Learning based Air Shower Reconstruction at the Pierre Auger Observatory
- 1. III. Physikalisches Institut A, RWTH Aachen (Germany)
Description
The surface detector of the Pierre Auger Observatory measures the footprint of ultra-high energy cosmic ray induced air showers on ground level. Furthermore, fluorescence telescopes allow hybrid detection of air showers and hence, for an independent crosscheck. Reconstructing observables sensitive to the cosmic ray mass, is a challenging task and mainly based on the fluorescence detector which, however, has a small duty cycle. Recently, great progress has been made in multiple fields of machine learning by using deep neural networks and associated techniques. Applying these new techniques on air shower physics provides a new and independent reconstruction. In this talk, we present AixNet, a deep convolutional neural network for the reconstruction of ultra-high energy cosmic rays properties. First, we assess the performance on CORSIKA based air showers, discuss the performance limit and compare the performance achieved on data by cross-calibrating with the fluorescence detector. Furthermore, we visualize the multidimensional differences between data and simulation using deep neural networks to understand differences in the reconstruction and prepare the simulation for refinement studies.
Availability note (English)
Available from: https://www.dpg-verhandlungen.de/Additional details
Identifiers
Publishing Information
- Journal Title
- Verhandlungen der Deutschen Physikalischen Gesellschaft
- Journal Issue
- Aachen 2019 issue
- Journal Page Range
- [1 p.]
- ISSN
- 0420-0195
- CODEN
- VDPEAZ
Conference
- Title
- Particle physics, didactics of physics, working group jDPG, working group physics, modern information technology and artificial intelligence
- Original Conference Title
- DPG-Fruehjahrstagung 2019 mit den folgenden Fachverbaenden und Arbeitskreisen: Teilchenphysik, Didaktik der Physik, Arbeitskreis jDPG, Arbeitskreis Physik, moderne Informationstechnologie und Kuenstliche Intelligenz
- Acronym
- DPG Spring meeting 2019 of the following divisions und working groups
- Dates
- 25-29 Mar 2019
- Place
- Aachen (Germany)
INIS
- Country of Publication
- Germany
- Country of Input or Organization
- Germany
- INIS RN
- 51001691
- Subject category
- S46: INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY;
- Resource subtype / Literary indicator
- Conference
- Quality check status
- Yes
- Descriptors DEI
- A CODES; C CODES; CALIBRATION; COMPUTERIZED SIMULATION; COSMIC RAY DETECTION; DATA VISUALIZATION; EXTENSIVE AIR SHOWERS; FLUORESCENCE; GROUND LEVEL; LEARNING; MANY-DIMENSIONAL CALCULATIONS; NEURAL NETWORKS; PERFORMANCE; TELESCOPES;
- Descriptors DEC
- COMPUTER CODES; COSMIC RADIATION; COSMIC SHOWERS; DATA ANALYSIS; DATA PROCESSING; DETECTION; EMISSION; IONIZING RADIATIONS; LEVELS; LUMINESCENCE; PHOTON EMISSION; PROCESSING; RADIATION DETECTION; RADIATIONS; SECONDARY COSMIC RADIATION; SHOWERS; SIMULATION;
Optional Information
- Notes
- Session: T 29.9 Di 18:00; Also available as printed version: Verhandlungen der Deutschen Physikalischen Gesellschaft v. 54(3)