The modern astrophysics is moving towards the enlarging of experiments and combining the channels for detecting the highest energy processes in the Universe. To obtain reliable data, the experiments should operate within several decades, which means that the data will be obtained and analyzed by several generations of physicists. Thus, for the stability of the experiments, it is necessary to properly maintain not only the data life cycle, but also the human aspects, for example, attracting, learning and continuity. To this end, an educational and outreach resource has been deployed in the framework of German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI).
The only way to study high-energy processes occurring within and outside the
Milky Way is to detect the radiation and ultra-high-energy particles generated
by these processes. The ux of ultrahigh-energy cosmic rays, gamma rays and
neutrinos interacting with the atmosphere gives rise to cascades of secondary
particles. Reaching the ground, these cascades can cover areas of tens of km2,
moreover, with an increase in the energy of the initial particle, their ux drops
sharply, reaching one particle per year per thousand km2. Thus, over the past few
years, experimental astrophysics of ultrahigh energies has been moving towards
the enlarging of experiments and combining the channels for detecting
highenergy processes named multi-messenger astronomy [
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The Baikal region is a unique place in Russia since two of the largest setups,
investigating three channels of multi-messenger astronomy, are deployed here:
the TAIGA gamma observatory [
To meet this challenge the new Baikal Multimessenger Lab was established at the Irkutsk State University (ISU) with the support of the Ministry of Education and Science of the Russian Federation this year. The declared missions of the Lab are: { Creation of a common framework for experiments in the Baikal region (Baikal
GVD and TAIGA); { Integration of these setups into full-stack multi-messenger astronomy; { Creation of a competitive school for astroparticle physics at ISU.
So, the important goal of the Lab is to attract more students to the astroparticle physics and train highly quali ed specialists in the eld of data processing and analysis for multi-messenger astronomy. The educational and outreach resource astroparticle.online contributes to the achievement of this goal.
This article is about the resource astroparticle.online [4], [5], its goals and application for Baikal region experiments. In Section 2 there is the description of the resource on the whole and Section 3 is devoted to the interactive part of the resource, developed for gamma-ray astronomy tasks using convolutional neural networks (CNN). 2
Web Resource astroparticle.online The deployment of the astroparticle.online resource (Fig. 1) was started in 2018 in the frame of the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) [6]. The resource is built on a free and open-source content management system WordPress. The servers of the platform are located at the Matrosov Institute for System Dynamics and Control Theory. The main target audience |- students, who are interested in astroparticle physics and would like to collaborate with the Baikal region astroparticle physics experiments.
The resource has several sections: News on astronomy and astrophysics (updated weekly), Science, Experiments, Projects dedicate to the Theory of messengers and astroparticle physics experiments and projects, respectively. These sections aim to attract younger students and schoolchildren and contain text materials, also video materials that are borrowed from other sources with referring to the source, quizzes for better assimilation of the material.
The section Online School, the largest one, is mostly original, partly based on the new course in astrophysics launched at the ISU in 2019. It has the following subsections: { Data Analysis ; { Lections; { Seminars ; { Labs; { Popular Science; { Tasks. 3
Ground-based gamma-ray astronomy studies very energetic radiation of galactic and extragalactic origin by specially designed telescopes, the so-called Imaging Air Cherenkov Telescopes (IACTs) [7]. With this technique, gamma-rays are observed on the ground optically via the Cherenkov light emitted by air-showers of secondary particles when a very-high-energy gamma-ray strikes the atmosphere. Gamma-rays of such energies contribute only a fraction below one per million to the ux of cosmic rays, most of which are protons [8]. Nevertheless, being particles without electric charge they can be extrapolated back to their origin, which makes them the best \messengers" of exotic and extreme processes and physical conditions in the Universe. That is why particle identi cation (gammaray discrimination against the cosmic-ray background) is an essential part of data analysis for the IACT technique.
The subsection Data Analysis contains a prototype of the Astroparticle CNN client developed for gamma-ray astronomy tasks as an interactive service for students. This prototype provides access to an on-line analysis of the gamma/hadron separation using convolutional neural networks developed as part of the GRADLCI project [9]. The Monte Carlo events of the TAIGA-IACT telescope are used as input for this prototype. The developed convolutional neural network gets the probability of the gamma reconstruction for each event as a result. There is also a possibility to check your skills in gamma/hadron separation using the telescope image.
A shower image is tted as an ellipse. The ellipse is characterized by its axes and has parameters: length, width, distance and the angular miss-alignment of the major axis. In comparison with hadron showers gamma-ray ones have more elliptic shape, less width and major axis pointed to the source. The neural network underlying the prototype takes these features into account.
The interactive service contains: { Guessing game on gamma/hadron separation (Fig. 2); { Instruction how to de ne gamma-event using telescope image; { Prepared datasets that are ready for downloading; { Application for processing your own dataset; { Tools:
Script for data visualization + instruction;
Script for converting data les to HDF5 format + instruction.
The back end of the service is deployed as a microservice in a Docker container. It is written in Python and is based on the Django framework. SQLite is used as the Database Management System, since it contains only information about preloaded datasets. The database includes two tables: a dataset table and an event table. The database structure is de ned by the model described using the Django framework. Tables are populated from HDF5 les with data sets during system deployment. Also during deployment of the system the event image les are generated. 4
The astroparticle.online resource is intended to become an education and outreach instrument for the new Baikal Multimessenger Lab as well as to advertise the experiments of the Baikal region such as the TAIGA gamma observatory and the Baikal-GVD neutrino telescope. We hope that this resource will attract more students to the astroparticle physics and will allow us to train highly qualied specialists in the eld of data processing for astrophysical experiments. This can solve a problem of great importance, namely, to prepare the above experiments for multi-messenger astronomy and for the interaction with other setups around the world.
The resource is lled with materials and tasks in astroparticle physics and its content is regularly updated. The prototype of the Astroparticle CNN client is developed. It is implemented in the resource astroparticle.online as an interactive service for illustrating one of the most complicated challenges in gamma-ray astronomy - the gamma/hadron separation.
Acknowledgements This work was supported by Russian Science Foundation Grant 18-41-06003 (Section 2, 3), by the Helmholtz Society Grant HRSF-0027 and by the Russian Federation Ministry of Science and High Education (project. FZZE-2020-0024). We are grateful to the members of the GRADLCI for the informational support of our activity. 4. Y. Kazarina et al., Towards the Baikal Open Laboratory in Astroparticle Physics,
CEUR Workshop Proceedings, Vol. 2406, pp. 1-6, 2019. 5. Y. Kazarina et al., Application of HUBzero platform for the educational process in astroparticle physics, CEUR Workshop Proceedings, Vol. 2267, pp. 553-557, 2018. 6. Bychkov, I. et al.Russian-German Astroparticle Data Life Cycle Initiative, Data 3(4), 56 (2018). 7. T. C. Weekes et al. Observation of TeV Gamma Rays from the Crab Nebula Using the
Atmospheric Cerenkov Imaging Technique, Astrophys. J., 342, pp.379{395, 1989. 8. E. Lorenz, R. Wagner Very-high energy gamma-ray astronomy, EPJ, H 37, pp.
459{513, 2012. 9. E.Postnikov et al. Gamma/Hadron Separation in Imaging Air Cherenkov Telescopes Using Deep Learning Libraries TensorFlow and PyTorch, Journal of Physics: Conference Series, Vol.1181, p. 012048, 2019.