Cosmic-Ray Extremely Distributed Observatory

MRNG: Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator

Privacy and security require not only strong algorithms but also reliable and readily available sources of randomness. To tackle this problem, one of the causes of single-event upsets is the utilization of a non-deterministic entropy source, specifically ultra-high energy cosmic rays. An adapted prototype based on existing muon detection technology was used as the methodology during the experiment and tested for its statistical strength. Our results show that the random bit sequence extracted from the detections successfully passed established randomness tests. The detections correspond to cosmic rays recorded using a common smartphone during our experiment. Despite the limited sample, our work provides valuable insights into the use of ultra-high energy cosmic rays as an entropy source.

The Practice of Detecting Potential Cosmic Rays Using CMOS Cameras: Hardware and Algorithms

In this paper, we discuss a practice of potential cosmic ray detection using off-the-shelves CMOS cameras. We discuss and presents the limitations of up-to-date hardware and software approaches to this task. We also present a hardware solution that we made for long-term testing of algorithms for potential cosmic ray detection. We have also proposed, implemented and tested a novel algorithm that enables real-time processing of image frames acquired by CMOS cameras in order to detect tracks of potential particles. We have compared our results with already published results and obtained acceptable results overcoming some limitation of already existing algorithms. Both source codes and data are available to download.

First Results on the Revealing of Cognate Ancestors among the Particles of the Primary Cosmic Rays That Gave Rise to Extensive Air Showers Observed by the GELATICA Network

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.

Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder

Cosmic rays were discovered by the Austrian physicist Victor Hess in 1912 in a series of balloon experiments performed between 1911 and 1912. Cosmic rays are an integral part of fundamental and applied research in the field of solar–terrestrial physics and space weather. Cosmic ray data are applied in different fields from the discovery of high-energy particles coming to Earth from space, and new fundamental symmetries in the laws of nature, to the knowledge of residual matter and magnetic fields in interstellar space. The properties of interplanetary space are determined from intensity variations, angular distribution, and other characteristics of galactic cosmic rays. The measure of cosmic ray flux intensity variability is used as one of the significant space weather factors. The negative impact of cosmic rays is also known. The negative impact can significantly increase the level of radiation hazard and pose a threat to astronauts, crews, and passengers of high-altitude aircraft on polar routes and to modern space equipment. Therefore, methods aimed at timely detection and identification of anomalous manifestations in cosmic rays are of particular practical relevance. The article proposes a method for analyzing cosmic ray variations and detecting anomalous changes in the rate of galactic cosmic ray arrival to the Earth. The method is based on a combination of the Autoencoder neural network with wavelet transform. The use of non-linear activation functions and the ability to flexibly change the structure of the network provide the ability of the Autoencoder to approximate complex dependencies in the recorded variations of cosmic rays. The article describes the numerical operations of the method implementation. Verification of the adequacy of the neural network model is based on the use of Box–Ljung Q-statistics. On the basis of the wavelet transform constructions, data-adaptive operations for detecting complex singular structures are constructed. The parameters of the applied threshold functions are estimated with a given confidence probability based on the α-quantiles of Student’s distribution. Using data from high-latitude neutron monitor stations, it is shown that the proposed method provides efficient detection of anomalies in cosmic rays during increased solar activity and magnetic storms. Using the example of a moderate magnetic storm on 10–11 May 2019, the necessity of applying different methods and approaches to the study of cosmic ray variations is confirmed, and the importance of taking them into account when making space weather forecast is shown.

Symmetries in the Superposition Model of Extensive Air Shower Development

According to the superposition principle, an extensive air shower initiated by a nucleus with energy E and mass number A can be approximated as the superposition of A proton-initiated showers each with energy E/A. The superposition principle for interactions of atomic nuclei proposes to describe nucleus-initiated extensive air showers using simulations performed for proton showers. Single detectors and systems working in tight coincidence mainly register events initiated by particles with very low energies, which are affected by major statistical fluctuations, such as those used in high schools for education and outreach purposes. Verifying whether the superposition principle is still a good approximation in the low-energy region is important for the validity of the interpretation of such measurements. We present results of the comparison of results of the superposition model with detailed simulations of showers with the CORSIKA program from the energy of 10 GeV. While the energy dependence of the mean shower parameters satisfies the superposition principle, the higher moments do not. A modification of the superposition model based on the wounded nucleon model, reducing these discrepancies, is proposed. The semi-analytical description of showers in the modified superposition model can give the density spectrum of cosmic ray particles, which is consistent with the measurements. In this paper, we present results both consistent with the superposition model and indicating the need for its modification. This modification is proposed and tested.

A Search for Cosmic Ray Bursts at 0.1 PeV with a Small Air Shower Array

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.

Constraints on Cosmic Ray Acceleration Capabilities of Black Holes in X-ray Binaries and Active Galactic Nuclei

Rotating black holes (BHs) are likely the largest energy reservoirs in the Universe as predicted by BH thermodynamics, while cosmic rays (CRs) are the most energetic among particles detected on Earth. Magnetic fields surrounding BHs combined with strong gravity effects, thanks to the spacetime symmetries, turn the BHs into powerful accelerators of charged particles. At the same time, in the age of multi-wavelength and multi-messenger astronomy, BHs and their environments have not yet been probed with CR messengers, despite being observed across most of the electromagnetic spectrum, and neutrino and gravitational waves. In this paper, we probe the acceleration capabilities of BHs in 8 galactic X-ray binaries and 25 local active galactic nuclei (AGNs) within 100 Mpc, based on the ultra-efficient regime of the magnetic Penrose process of a BH energy extraction combined with observational data. We find that the maximum energy of the galactic BHs can reach only up to the knee of the CR spectrum, including supermassive BH Sgr A*at the Galactic Center. On the other hand, for supermassive BHs in AGNs, we find that the mean energy of primary CRs is of the order of 1019 eV. It is therefore likely that local supermassive BHs give sufficient contribution to the ankle—a sharp change in the slope of the cosmic ray spectrum around 1018.6 eV energy. We also discuss the energy losses of primary CRs close to the acceleration zones. In the galactic BH cases, it is likely dominated by synchrotron radiation losses.

Method of Wavelet-Decomposition to Research Cosmic Ray Variations: Application in Space Weather

Since their discovery, cosmic rays have been an integral part of the development of fundamental physics, from the discovery of radiation coming to the Earth from outer space and the identification of high-energy particles in it, as well as new fundamental symmetries in the laws of nature, to the knowledge of residual matter and magnetic fields in interstellar space. Cosmic rays are used in a number of fundamental and applied research in solar-terrestrial physics and are important in the research of the near-Earth space processes. Cosmic ray variations observed on the Earth’s surface are an integral result of various solar, heliospheric, magnetospheric and atmospheric phenomena. The most significant changes in cosmic ray parameters are caused by coronal mass ejections and subsequent changes in the parameters of the interplanetary magnetic field and solar wind. Therefore, the study of cosmic rays makes it possible to obtain valuable information about the processes in the near-Earth space and in the Earth’s magnetosphere during disturbed periods. This article proposes a method for analyzing cosmic ray variations. It is based on the use of wavelet data decomposition operations and their combination with threshold functions. By using adaptive thresholds, the operations for detecting anomalous changes in data and for suppressing the noise were developed. Anomalies in cosmic rays can cause radiation hazard for astronauts, radio communication failures, as well as malfunctions in satellites, leading to the loss of orientation and destruction. Therefore, the task of timely diagnostics of anomalies is urgent. The paper describes the algorithms for the implementation of the method and shows their application in the space weather problem. We used data from the network of ground stations of neutron monitors. The efficiency of the method for detecting abnormal changes of different amplitudes and durations is shown. Application of the method made it possible to detect clearly and to evaluate Forbush effects in cosmic rays, which precede the onset of magnetic storms of various nature and strength.

Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors

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%.

Investigations of Muon Flux Variations Detected Using Veto Detectors of the Digital Gamma-rays Spectrometer

This paper presents the results of cosmic ray muons flux monitoring registered by a digital gamma-ray spectrometer’s active shield made of five large plastic scintillators. In traditional, i.e., analogue active shields working in anticoincidence mode with germanium detectors, the generated data are used only as a gating signal and are not stored. However, thanks to digital acquisition applied in designed novel gamma-ray spectrometers enabling offline studies, it has not only become possible to use generated data to reduce the germanium detector background (cosmic rays veto system) but also to initialize long-term monitoring of the muon flux intensity. Furthermore, various analyses methods prove the relevance of the acquired data. Fourier analyses revealed the presence of daily (24 h), near-monthly (27 days) and over bi-monthly (68 days) cycles.

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

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.

Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network

In this paper, we describe the convolutional neural network (CNN)-based approach to the problems of categorization and artefact reduction of cosmic ray images obtained from CMOS sensors used in mobile phones. As artefacts, we understand all images that cannot be attributed to particles’ passage through sensor but rather result from the deficiencies of the registration procedure. The proposed deep neural network is composed of a pretrained CNN and neural-network-based approximator, which models the uncertainty of image class assignment. The network was trained using a transfer learning approach with a mean squared error loss function. We evaluated our approach on a data set containing 2350 images labelled by five judges. The most accurate results were obtained using the VGG16 CNN architecture; the recognition rate (RR) was 85.79% ± 2.24% with a mean squared error (MSE) of 0.03 ± 0.00. After applying the proposed threshold scheme to eliminate less probable class assignments, we obtained a RR of 96.95% ± 1.38% for a threshold of 0.9, which left about 62.60% ± 2.88% of the overall data. Importantly, the research and results presented in this paper are part of the pioneering field of the application of citizen science in the recognition of cosmic rays and, to the best of our knowledge, this analysis is performed on the largest freely available cosmic ray hit dataset.