Cosmic ray data collected by the KASCADE air shower experiment are competitive in terms of quality and statistics with those of modern observatories. We present a novel mass composition analysis based on archival data acquired from 1998 to 2013 provided by the KASCADE Cosmic ray Data Center (KCDC). The analysis is based on modern machine learning techniques trained on simulation data provided by KCDC. We present spectra for individual groups of primary nuclei, the results of a search for anisotropies in the event arrival directions taking mass composition into account, and search for gamma-ray candidates in the PeV energy domain.

Radio detection of air showers produced by ultra-high energy cosmic rays is a cost-effective technique for the next generation of sparse arrays. The performance of this technique strongly depends on the environmental background, which has different constituents, namely anthropogenic radio frequency interference, synchrotron galactic radiation and others. These components have recognizable features, which can help for background suppression. A powerful method for handling this is the application of convolution neural networks with a specific architecture called autoencoder. By suppressing unwanted signatures, the autoencoder keeps the signal-like ones. We have successfully developed and trained an autoencoder, which is now applied to the data from Tunka-Rex. We show the procedures of the training and optimization of the network including benchmarks of different architectures. Using the autoencoder, we improved the standard analysis of Tunka-Rex in order to lower the threshold of the detection. This enables the reconstructing of sub-threshold events with energies lower than 0.1 EeV with satisfactory angular and energy resolutions.

We present new insight into the ongoing machine learning analysis of KASCADE experiment archival data, that contain air shower events with ∼ 1 − 100 PeV primary energy. The aim of the study is to improve the accuracy of high-energy cosmic rays mass composition reconstruction with respect to the standard KASCADE technique. We introduce five mass groups: protons, helium, carbon, silicon and iron nuclei and interpret the reconstruction process as a classification task. We employ a random forest technique as well as two promising neural network architecturesa self-attention perceptron and a convolutional neural network. These models are being trained with KASCADE CORSIKA simulations. We examine the behavior of the mass composition reconstruction for several hadronic interaction models and additionally check the credibility of our methods with a small "unblinded" part of the real KASCADE data. *** 27th European Cosmic Ray Symposium -ECRS *** *** 25-29 July 2022 *** *** Nijmegen, the Netherlands *** * Speaker

No abstract

We present the first steps of a search for high-energy (> 1 PeV) gamma rays in archival data of the KASCADE experiment. With the data collected from 1996 to 2013 the KASCADE statistics is comparable with that of modern observatories. The data is provided by the KASCADE Cosmic ray Data Center (KCDC) and publicly available. We employ methods of machine learning to distinguish between air showers produced by hadronic and gamma-ray primaries. For that we design primary particle type classifiers and train them with the KASCADE Monte-Carlo simulations. We compare results of several deep learning methods: a graph neural network, a self-attention network and a compact convolutional transformer. The level of hadronic background suppression with respect to gamma-ray signal in the best of these methods exceeds that of the original KASCADE method by more than an order of magnitude. *** 27th European Cosmic Ray Symposium -ECRS *** *** 25-29 July 2022 *** *** Nijmegen, the Netherlands *** * Speaker