Andrey Demichev

The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.

High-energy particles hitting the upper atmosphere of the Earth produce extensive air showers that can be detected from the ground level using imaging atmospheric Cherenkov telescopes. The images recorded by Cherenkov telescopes can be analyzed to separate gamma-ray events from the background hadron events. Many of the methods of analysis require simulation of massive amounts of events and the corresponding images by the Monte Carlo method. However, Monte Carlo simulation is computationally expensive. The data simulated by the Monte Carlo method can be augmented by images generated using faster machine learning methods such as generative adversarial networks or conditional variational autoencoders. We use a conditional variational autoencoder to generate images of gamma events from a Cherenkov telescope of the TAIGA experiment. The variational autoencoder is trained on a set of Monte Carlo events with the image size, or the sum of the amplitudes of the pixels, used as the conditional parameter. We used the trained variational autoencoder to generate new images with the same distribution of the conditional parameter as the size distribution of the Monte Carlo-simulated images of gamma events. The generated images are similar to the Monte Carlo images: a classifier neural network trained on gamma and proton events assigns them the average gamma score 0.984, with less than 3% of the events being assigned the gamma score below 0.999. At the same time, the sizes of the generated images do not match the conditional parameter used in their generation, with the average error 0.33.

Very-high-energy gamma ray photons interact with the atmosphere to give rise to cascades ofsecondary particles – extensive air showers (EASs), which in turn generate very short flashes ofCherenkov radiation. This flashes are detected on the ground with Imaging Air Cherenkov Telescopes(IACTs). In the TAIGA experiment, in addition to images directly detected and recorded by theexperimental facilities, images obtained as a result of simulation are used extensively. Earlier weapplied a machine learning technique called Generative Adversarial Networks (GAN) to quicklygenerate images of gamma events for the TAIGA experiment. The initial analysis of the generatedimages showed the applicability of the method, but revealed some features that require additionalrefinement of the network. In particular, it was important to teach the network that in our case imageshave a specific shape and orientation. In this paper we discuss the possibility of improving thegenerated images by preprocessing the training dataset. We also present an example of a GAN builtand trained with these requirements in mind. Testing the results using third-party software showed thatmore than 95% of the generated images were found to be correct, while the generation is quite fast:after training the network creates about 400 event images in 1 second.

The work presents a solution for completely decentralized data management systems in geographically distributed environments with administratively unrelated or loosely related user groups and in conditions of partial or complete lack of trust between them. The solution is based on the integration of blockchain technology, smart contracts and provenance metadata driven data management. Architecture, operation principles and algorithms developed provides fault-tolerant, safe and reliable management of provenance metadata, control of operations with data files, as well as resource access management in collaborative distributed computing systems. The latter refer to distributed systems formed by combining into a single pool of computer resources of various organizations (institutions) to work together in the framework of some project.

The paper suggests a new approach based on blockchain technologies and smart contracts to creation of a distributed system for managing provenance metadata, as well as access rights to data in distributed storages, which is fault-tolerant, safe and secure from the point of view of preservation of metadata records from accidental or intentional distortions. The implementation of the proposed approach is based on the permissioned blockchains and on the Hyperledger Fabric blockchain platform in conjunction with Hyperledger Composer.

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.

For the successful development of the astrophysics and, accordingly, for obtaining more complete knowledge of the Universe, it is extremely important to combine and comprehensively analyze information of various types (e.g., about charged cosmic particles, gamma rays, neutrinos, etc.) obtained by using divers large-scale experimental setups located throughout the world. It is obvious that all kinds of activities must be performed continually across all stages of the data life cycle to help support effective data management, in particular, the collection and storage of data, its processing and analysis, refining the physical model, making preparations for publication, and data reprocessing taking refinement into account. In this paper we present a general approach to construction and the architecture of a system to be able to collect, store, and provide users' access to astrophysical data. We also suggest a new approach to the construction of a metadata registry based on the blockchain technology.

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.