Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning

Abstract

Radio emission from air showers enables measurements of cosmic particle kinematics and identity. The radio signals are detected in broadband Megahertz antennas among continuous background noise. We present two deep learning concepts and their performance when applied to simulated data. The first network classifies time traces as signal or background. We achieve a true positive rate of about 90% for signal-to-noise ratios larger than three with a false positive rate below 0.2%. The other network is used to clean the time trace from background and to recover the radio time trace originating from an air shower. Here we achieve a resolution in the energy contained in the trace of about 20% without a bias for $80\%$ of the traces with a signal. The obtained frequency spectrum is cleaned from signals of radio frequency interference and shows the expected shape.

arXiv comment20 pages, 13 figures, resubmitted to JINST

References in corpus (34)

• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• The Pierre Auger Cosmic Ray Observatory
• Measurement of the energy spectrum of cosmic rays above 10^18 eV using the Pierre Auger Observatory
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Detection and imaging of atmospheric radio flashes from cosmic ray air showers
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• A Convolutional Neural Network Neutrino Event Classifier
• Radio detection of Cosmic-Ray Air Showers and High-Energy Neutrinos
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Simulating radio emission from air showers with CoREAS
• Radio detection of cosmic ray air showers in the digital era
• Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory
• Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy
• Measurement of cosmic-ray air showers with the Tunka Radio Extension (Tunka-Rex)
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• Enhanced Higgs to ${\tau }^{+}{\tau }^{-}$ Searches with Deep Learning
• Radioelectric Field Features of Extensive Air Showers Observed with CODALEMA
• Advanced functionality for radio analysis in the Offline software framework of the Pierre Auger Observatory
• A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
• Reconstruction of the energy and depth of maximum of cosmic-ray air-showers from LOPES radio measurements
• Simulation of Radiation Energy Release in Air Showers
• Radio measurements of the energy and the depth of the shower maximum of cosmic-ray air showers by Tunka-Rex
• The radio emission pattern of air showers as measured with LOFAR - a tool for the reconstruction of the energy and the shower maximum
• First Experimental Characterization of Microwave Emission from Cosmic Ray Air Showers
• Jet-Parton Assignment in ttH Events using Deep Learning
• A Development Environment for Visual Physics Analysis
• A field study of data analysis exercises in a bachelor physics course using the internet platform VISPA
• $X_{\text{max}}$ reconstruction from amplitude information with AERA
• Results and Perspectives of the Auger Engineering Radio Array
• Experiment Software and Projects on the Web with VISPA

Cited by in corpus (10)

• The Giant Radio Array for Neutrino Detection (GRAND): Science and Design
• Ultra-High-Energy Cosmic Rays: The Intersection of the Cosmic and Energy Frontiers
• Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks
• Radio-detection of neutrino-induced air showers: the influence of topography
• Machine learning study to identify collective flow in small and large colliding systems
• Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data
• Tunka-Rex Virtual Observatory
• Radio Detection of ultra-high-energy Cosmic-Ray Air Showers
• AI-Enhanced Self-Triggering for Extensive Air Showers: Performance and FPGA Feasibility
• Data-driven detector signal characterization with constrained bottleneck autoencoders