Cloud infrastructure of INP'S Astana branch - PE "NULITS" and its integration with distributed JINR cloud infrastructure

The article describes the Institute of Nuclear Physics’ (INP) Astana branch - private establishments “Nazarbayev University Library and IT services” (PE NULITS) cloud and its integration with the distributed cloud infrastructure consisting of the Laboratory of Information Technologies of the Joint Institute for Nuclear Research (JINR) cloud as well as clouds of some JINR Member State organizations. It explains a motivation of that work, an approach it is based on, working plan of the integration

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Evolution and Perspectives of the Service for Parallel Applications Running at JINR Multifunctional Information and Computing Complex

Nowadays scientists use cloud computing as a routine tool in a lot of fields of their research. Various Multifunctional Information and Computing Complex (MICC) resources are provided for JINR users to perform a wide range of scientific computations. The JINR cloud service for parallel applications was developed in order to simplify scientists’ work on running similar tasks but on different MICC resources and also to speed up the process of reaching significant results. There are several components with a flexible and modular architecture that allow running a various number of applications using different types of computational resources. The service is constantly developing and improving with the help of the users’ feedback. Some changes in web-interface were made to improve users’ experience: there was added the possibility to choose a certain type of particular application, to set a description for a job, to run multiple tasks, to notify a user about successful job submission and its completion. Moreover, accessibility of job results was reworked: when the job is done, its output is uploaded at the external file storage, where it becomes available at the auto-generated unique URL for downloading by the user and further analysis and/or visualization

Evolution and Perspectives of the Service for Parallel Applications Running at JINR Multifunctional Information and Computing Complex

Nowadays scientists use cloud computing as a routine tool in a lot of fields of their research. Various Multifunctional Information and Computing Complex (MICC) resources are provided for JINR users to perform a wide range of scientific computations. The JINR cloud service for parallel applications was developed in order to simplify scientists’ work on running similar tasks but on different MICC resources and also to speed up the process of reaching significant results. There are several components with a flexible and modular architecture that allow running a various number of applications using different types of computational resources. The service is constantly developing and improving with the help of the users’ feedback. Some changes in web-interface were made to improve users’ experience: there was added the possibility to choose a certain type of particular application, to set a description for a job, to run multiple tasks, to notify a user about successful job submission and its completion. Moreover, accessibility of job results was reworked: when the job is done, its output is uploaded at the external file storage, where it becomes available at the auto-generated unique URL for downloading by the user and further analysis and/or visualization

Integrated cloud infrastructure of the LIT JINR, PE “NULITS” and INP's Astana branch

The article describes the distributed cloud infrastructure deployed on the basis of the resources of the Laboratory of Information Technologies of the Joint Institute for Nuclear Research (LIT JINR) and some JINR Member State organizations. It explains a motivation of that work, an approach it is based on, lists of its participants among which there are private entity “Nazarbayev University Library and IT services” (PE “NULITS”) Autonomous Education Organization “Nazarbayev University” (AO NU) and The Institute of Nuclear Physics’ (INP's) Astana branch

Integrated cloud infrastructure of the LIT JINR, PE “NULITS” and INP's Astana branch

The article describes the distributed cloud infrastructure deployed on the basis of the resources of the Laboratory of Information Technologies of the Joint Institute for Nuclear Research (LIT JINR) and some JINR Member State organizations. It explains a motivation of that work, an approach it is based on, lists of its participants among which there are private entity “Nazarbayev University Library and IT services” (PE “NULITS”) Autonomous Education Organization “Nazarbayev University” (AO NU) and The Institute of Nuclear Physics’ (INP's) Astana branch

Creating a Unified Educational Environment for Training IT Specialists of Organizations of the JINR Member States in the Field of Cloud Technologies

Modern science heavy relies on the usage of information technologies (IT). It is important to organize knowledge transfer from IT specialists to non-IT and to less educated and/or skilled IT ones. Nowadays a speed of IT development (as well as achievements of the results these IT are used for) can be sufficiently increased by joining efforts and resources of cooperating organizations, which solve similar tasks. An important aspect of such cooperation is an experience exchange and knowledge transfer, which can be obtained by participating in conferences, seminars, workshops, master classes, etc. That article provides information on the activities around a cloud infrastructure created at the Laboratory of Information Technologies of the Joint Institute for Nuclear Research (JINR). It describes a purpose of its creation, implemented features, use-cases and training events it is used in. A relevance of the JINR and its Member State organizations clouds integration based on the DIRAC middleware is described too. Particular attention is paid to a process of knowledge transfer from JINR colleagues to fellows of other participating institutions through the organizing and holding of seminars, schools, conferences, round tables as well as semestrial training courses for students

Present Status and Main Directions of the JINR Cloud Development

The JINR cloud is growing not only in terms of the amount of resources, but also in the number of activities it is used for, namely, COMPASS production system services, a data management system of the UNECE ICP Vegetation, a service for disease detection of agricultural crops through the use of advanced machine learning approaches, a service for scientific and engineering computations, a service for data visualization based on Grafana, the jupyterhub head and execute nodes for it, gitlab and its runners, as well as some others. Apart from that, there was a successful attempt to deploy a virtual machine in the JINR cloud with a GPU card passed through from the server for developing and running machine and deep learning algorithms for the JUNO experiment. Moreover, the JINR distributed information and computing environment, combining resources from JINR Member State organizations with the help of the DIRAC grid interware, began to be used for running BM@N and MPD experiment jobs. The software distribution on these remote resources was performed using the CernVM File System. All these topics are covered in detail