Measurement of the Depth of Maximum of Air-Shower Profiles with energies between and eV using the Surface Detector of the Pierre Auger Observatory and Deep Learning
arXiv:2406.06319 · doi:10.1103/PhysRevD.111.022003
Abstract
We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV= eV) using the distributions of the depth of shower maximum . The analysis relies on events recorded by the Surface Detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the data set offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the Fluorescence Detector, this enables the first measurement of the evolution of the mean and the standard deviation of the distributions up to 100 EeV. Our findings are threefold: (1.) The evolution of the mean logarithmic mass towards a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (2.) The evolution of the fluctuations of towards a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in at the highest energies. (3.) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum.
Version accepted for publication in Phys. Rev. D, 29 pages, 19 figures, 5 tables
References in corpus (22)
- Depth of Maximum of Air-Shower Profiles at the Pierre Auger Observatory: Measurements at Energies above 10^17.8 eV
- Observation of high-energy neutrinos from the Galactic plane
- The Cosmic Ray Energy Spectrum Observed with the Surface Detector of the Telescope Array Experiment
- Testing Hadronic Interactions at Ultrahigh Energies with Air Showers Measured by the Pierre Auger Observatory
- Measurement of the cosmic-ray energy spectrum above eV using the Pierre Auger Observatory
- Enhancing Gravitational-Wave Science with Machine Learning
- Features of the energy spectrum of cosmic rays above eV using the Pierre Auger Observatory
- The energy spectrum of cosmic rays beyond the turn-down around eV as measured with the surface detector of the Pierre Auger Observatory
- Calibration of the Surface Array of the Pierre Auger Observatory
- AugerPrime: the Pierre Auger Observatory Upgrade
- Measurement of the fluctuations in the number of muons in extensive air showers with the Pierre Auger Observatory
- A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
- Constraining the sources of ultra-high-energy cosmic rays across and above the ankle with the spectrum and composition data measured at the Pierre Auger Observatory
- A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
- Event reconstruction for KM3NeT/ORCA using convolutional neural networks
- Deep-Learning based Reconstruction of the Shower Maximum using the Water-Cherenkov Detectors of the Pierre Auger Observatory
- Reconstruction of Events Recorded with the Surface Detector of the Pierre Auger Observatory
- Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
- Testing Hadronic-Model Predictions of Depth of Maximum of Air-Shower Profiles and Ground-Particle Signals using Hybrid Data of the Pierre Auger Observatory
- Inference of the Mass Composition of Cosmic Rays with energies from to eV using the Pierre Auger Observatory and Deep Learning
- Sibyll
- The spectra and composition of Ultra High Energy Cosmic Rays and the measurement of the proton-air cross section
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