Search

37th International Cosmic Ray Conference, ICRC 2021, Berlin, online, Germany, 12 Jul 2021 - 23 Jul 2021; Proceedings of Science / International School for Advanced Studies (ICRC2021), 938 (2021). doi:10.22323/1.395.0938, Challenges faced by researchers in multi-messenger astroparticle physics include: computing-intensive search and preprocessing related to the diversity of content and formats of the data from different observatories as well as to data fragmentation over separate storage locations; inconsistencies in user interfaces for data retrieval; lack of the united infrastructure solutions suitable for both data gathering and online analysis, e.g. analyses employing deep neural networks. In order to address solving these issues, the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) was created. In addition, we support activities for communicating our research field to the public. The approaches proposed by the project are based on the concept of data life cycle, which assumes a particular pipeline of data curation used for every unit of the data from the moment of its retrieval or creation through the stages of data preprocessing, analysis, publishing and archival. The movement towards unified data curation schemes is essential to increase the benefits gained in the analysis of geographically distributed or content-diverse data. Within the project, an infrastructure for effective astroparticle data curation and online analysis was developed. Using it, first results on deep-learning based analysis were obtained., Published by SISSA, Trieste

36th International Cosmic Ray Conference, ICRC2019, Madison, USA, 24 Jul 2019 - 1 Aug 2019; PoS(ICRC2019)284 (2019)., A data life cycle (DLC) is a high-level data processing pipeline that involves data acquisition, event reconstruction, data analysis, publication, archiving, and sharing. For astroparticle physics a DLC is particularly important due to the geographical and content diversity of the research field. A dedicated and experiment spanning analysis and data centre would ensure that multi-messenger analyses can be carried out using state-of-the-art methods. The German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) is a joint project of the KASCADE-Grande and TAIGA collaborations, aimed at developing a concept and creating a DLC prototype that takes into account the data processing features specific for the research field. An open science system based on the KASCADE Cosmic Ray Data Centre (KCDC), which is a web-based platform to provide the astroparticle physics data for the general public, must also include effective methods for distributed data storage algorithms and techniques to allow the community to perform simulations and analyses with sophisticated machine learning methods. The aim is to achieve more efficient analyses of the data collected in different, globally dispersed observatories, as well as a modern education to Big Data Scientist in the synergy between basic research and the information society. The contribution covers the status and future plans of the initiative.

Data 3(4), 56 (2018). doi:10.3390/data3040056, Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in the era of Big Data and of multi-messenger analysis in astroparticle physics. We propose an open science web platform called ASTROPARTICLE.ONLINE which enables us to publish, store, search, select, and analyze astroparticle data. In the first stage of the project, the following components of a full data life cycle concept are under development: describing, storing, and reusing astroparticle data; software to perform multi-messenger analysis using deep learning; and outreach for students, post-graduate students, and others who are interested in astroparticle physics. Here we describe the concepts of the web platform and the first obtained results, including the meta data structure for astroparticle data, data analysis by using convolution neural networks, description of the binary data, and the outreach platform for those interested in astroparticle physics. The KASCADE-Grande and TAIGA cosmic-ray experiments were chosen as pilot examples., Published by MDPI, Basel

Modern experimental astroparticle physics features large-scale setups measuring different messengers, namely high-energy particles generated by cosmic accelerators (e.g. supernova remnants, active galactic nuclei, etc): cosmic and gamma rays, neutrinos and recently discovered gravitational waves. Ongoing and future experiments are distributed over the Earth including ground, underground/underwater setups as well as balloon payloads and spacecrafts. The data acquired by these experiments have different formats, storage concepts and publication policies. Such differences are a crucial issue in the era of big data and of multi-messenger analysis strategies in astroparticle physics. We propose a service ASTROPARTICLE.ONLINE in the frame of which we develop an open science system which enables to publish, store, search, select and analyse astroparticle physics data. The cosmic-ray experiments KASCADE-Grande and TAIGA were chosen as pilot experiments to be included in this framework. In the first step of our initiative we will develop and test the following components of the full data life cycle concept: (i) describing, storing and reusing of astroparticle data; (ii) software for performing multi-experiment and multi-messenger analyses like deep-learning methods; (iii) outreach including example applications and tutorial for students and scientists outside the specific research field. In the present paper we describe the concepts of our initiative, and in particular the plans toward a common, federated astroparticle data storage.

This paper considers current trends in creating systems for convenient and secure remote submission (by authorized users) of jobs to computing resources of different types, including supercomputers, clusters, cloud resources, storages, databases, and grid infrastructures. Presently, large computing and storage resources are capable of solving, on their own, the majority of practical problems in the field of science and technology. Therefore, the focus in developing next-generation middleware shifts from global grid systems towards creating convenient and efficient means of access to large individual resources. A web platform for remote access to computing resources is a set of special web services and application-oriented web interfaces. Such web platforms, on a centralized basis, provide users with a number of services interrelated by a specific application area, principle of access, and interface. In this paper, general principles of creating such platforms are considered and some particular implementations of web platforms are briefly described.

The paper deal with a new measurement method for rotation speed and movements of shafts using non-contact eddy current sensors. The main advantages and shortcomings of this method are considered. The prospects of use of this method for various practical applications are analyzed. Special attention in the work is paid to errors of measurements that arise during the using of this technique and resistance of this method to impact of noise. Possible paths for improvement of characteristics of this method are presented. Now proposed method assumes determination of rotation speed with inaccuracy about 0.1 RPM and the space provision of a shaft with inaccuracy of 0.1 mm when using only one sensor.

The paper examines the current trends in designing of systems for convenient and secure remote job submission to various computer resources, including supercomputers, computer clusters, cloud resources, data storages and databases, and grid infrastructures by authorized users, as well as remote job monitoring and obtaining the results. Currently, high-perfomance computing and storage resources are capable of solving independently the majority of practical problems in the field of science and technology. Therefore, the focus in the development of a new generation of middleware shifts from the global grid systems to building convenient and efficient web platforms for remote access to individual computing resources. The paper examines the general principles of the construction and briefly describes some of the specific implementations of the web platforms., 10 pages; 4th International Young Scientists Conference on Computational Science (YSC 2015)

In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join., 6 pages, 2 figures. Submitted to JPCS, 26th Extended European Cosmic Ray Symposium and 35th Russian Cosmic Ray Conference (E+CRS 2018 / RCRC 2018), Barnaul - Belokurikha, July 6 - 10, 2018

A computer algebra algorithm for indefinite summation of rational functions based on complete factorization of denominators is proposed. For a given f, the algorithm finds two rational functions g, r such that f = g(x + 1) − g(x) + r and the degree of the denominator of r is minimal. A modification of the algorithm is also proposed that additionally minimizes the degree of the denominator of g. Computational complexity of the algorithms without regard to denominator factorization is shown to be O(m2), where m is the degree of the denominator of f.

An algorithm of indefinite summation of rational functions is proposed. For a given function f(x), it constructs a pair of rational functions g(x) and r(x) such that f(x) = g(x + 1) ? g(x) + r(x), where the degree of the denominator of r(x) is minimal, and, when this condition is satisfied, the degree of the denominator of g(x) is also minimal.

The existence of proper terms that have homogeneous Zeilberger recurrences with telescoping operators of arbitrarily large order is proved. The correctness of Zeilberger's algorithm in the homogeneous case is proved. An extension of the algorithm for computing such recurrences more efficiently is proposed.

The paper presents an algorithm for improvement (degree reduction) of universal denominators, which are used, for example, for constructing rational solutions of linear differential and difference systems with polynomial coefficients. A variant of Zeilberger's algorithm is described; it uses construction of universal denominator instead of application of Gosper's algorithm.

The problem of additive decomposition of rational functions minimizing the degree of the remainder's denominator with the additional minimization of the degree of the remainder's numerator is considered. An algorithm that solves this problem under certain additional assumptions is proposed. This algorithm is implemented in Maple.

This paper presents a project of developing the web platform called WebNLO for computer modeling of nonlinear optics phenomena. We discuss a general scheme of the platform and a model for interaction between the platform modules. The platform is built as a set of interacting RESTful web services (SaaS approach). Users can interact with the platform through a web browser or command line interface. Such a resource has no analogues in the field of nonlinear optics and will be created for the first time therefore allowing researchers to access high-performance computing resources that will significantly reduce the cost of the research and development process.

We propose an approach based on the analysis of cost-quality tradeoffs for efficiency comparisons of various algorithms for small-world interconnection network construction. A number of both known in the literature and original algorithms for complex small-world networks construction are shortly reviewed and compared. Networks constructed on the basis of these algorithms have a basic structure of 1D regular lattice with additional shortcuts providing the small-world properties. It is shown that networks proposed in this work have the best cost-quality ratio in the considered class.

Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between the events caused by gamma rays and by hadrons and to infer the parameters of the event such as the energy of the primary particle. We use convolutional neural networks (CNNs) to analyze Monte Carlo-simulated images from the telescopes of the TAIGA experiment. The analysis includes selection of the images corresponding to the showers caused by gamma rays and estimating the energy of the gamma rays. We compare performance of the CNNs using images from a single telescope and the CNNs using images from two telescopes as inputs., In Proceedings of 5th International Workshop on Deep Learning in Computational Physics (DLCP2021), 28-29 June, 2021, Moscow, Russia