Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.

The Cosmic Ray Extremely Distributed Observatory (CREDO) pursues a global research strategy dedicated to the search for correlated cosmic rays, so-called Cosmic Ray Ensembles (CRE). Its general approach to CRE detection does not involve any a priori considerations, and its search strategy encompasses both spatial and temporal correlations, on different scales. Here we search for time clustering of the cosmic ray events collected with a small sea-level extensive air shower array at the University of Adelaide. The array consists of seven one-square-metre scintillators enclosing an area of 10 m × 19 m. It has a threshold energy ~0.1 PeV, and records cosmic ray showers at a rate of ~6 mHz. We have examined event arrival times over a period of over 2.5 years in two equipment configurations (without and with GPS timing), recording ~300 k events and ~100 k events. We determined the event time spacing distributions between individual events and the distributions of time periods which contained specific numbers of multiple events. We find that the overall time distributions are as expected for random events. The distribution which was chosen a priori for particular study was for time periods covering five events (four spacings). Overall, these distributions fit closely with expectation, but there are two outliers of short burst periods in data for each configuration. One of these outliers contains eight events within 48 s. The physical characteristics of the array will be discussed together with the analysis procedure, including a comparison between the observed time distributions and expectation based on randomly arriving events.

For the data on the observation times and directions of the motion of extensive air showers, which are observed at two stations of the GELATICA network, for the first time we apply the method we have developed previously for identifying pairs of mutually remote extensive air showers, the ancestor particles of which arose, possibly, in a single process. A brief description of the GELATICA network, a review of the properties of used samples of data on shower observations at two stations of the network during the 2019–2021 session, and the result of applying the above method to them are given. Some properties of a single peculiar pair of remote showers are discussed. A side question arose about the cause of the observed temporal asymmetry in the locations of the regions of mutual approach of independent primary cosmic ray particles.

One of the main objectives of the CREDO project is to register cosmic-ray cascades in many distributed detectors in the search for so-called Cosmic-Ray Ensembles (CRE). This requires precise knowledge of the probability of detection of individual Extensive Air Showers (EAS) in a very wide range of energies and an analysis of their correlations. The standard approach based on detailed and extensive simulations is not possible for many such systems; thus, a faster method is developed. Knowing the characteristics of EAS from more general simulations, any required probability is calculated. Such probability depends on particle density at a given point, which is a function of the distance from the centre of the cascade, the energy, mass and the zenith angle of the primary cosmic-ray particle. It is necessary to use proper distribution of the number of secondary particles reaching the ground and their fluctuations. Finally, to calculate the total probability of EAS detection, the primary cosmic-ray spectrum and abundance of various particles in it have to be taken into account. The effective probability can be used to estimate the expected number of EAS events measured by a set of small detectors. In this work, results from several versions of calculations, with different complexity levels, are presented and compared with the first measurement performed with a test detector system. These results confirm that the majority of events observed with this small detector array are caused by cosmic-ray particles with very high energies. Such analysis can be also useful for the design of more effective systems in the future. Slightly larger systems of simple detectors may be used to distinguish cascades initiated by photons from those started from other primary cosmic-ray particles.

Cosmic rays interact with fields and background radiation as they propagate in space, producing particle cascades of various sizes, shapes and constituents. The potential observation of at least parts of such phenomena, referred to as cosmic-ray ensembles (CRE), from Earth would open a new channel of cosmic-ray investigation, since it might be a manifestation of fundamental symmetries of nature. Research dedicated to CRE is one of the main scientific objectives of the Cosmic-Ray Extremely Distributed Observatory (CREDO) Collaboration, and with this article we address one of the cornerstones of the relevant scientific program: the simulation method dedicated to CRE studies. Here we focus on CRE resulting from synchrotron radiation by high energy electrons as one of the most prevalent energy loss processes. Providing the example of simulation output analysis, we demonstrate the advantages of our approach as well as discuss the possibility of generalization of current research.