Digital Twins in the Practice of High-Energy Physics Experiments: A Gas System for the Multipurpose Detector

The digital twins technology delivers a new degree of freedom into system implementation and maintenance practice. Using this approach, a technological system can be efficiently modeled and simulated. Furthermore, such a twin offline system can be efficiently used to investigate real system issues and improvement opportunities, e.g., improvement of the existing control system or development of a new one. This work describes the development of a control system using the digital twins methodology for a gas system delivering a specific mixture of gases to the time-of-flight (ToF) multipurpose detector (MPD) used during high-energy physics experiments in the Joint Institute for Nuclear Research (Dubna, Russia). The gas system digital twin was built using a test stand and further extended into target full-scale installation planned to be built in the near future. Therefore, conducted simulations are used to validate the existing system and to allow validation of the planned new system. Moreover, the gas system digital twin enables testing of new control opportunities, improving the operation of the target gas system.

A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

Thirty years of Internet development. Lessons for Democracy: Internet as an opportunity for democratization of social life

This year marks thirty years since Tim Berners Lee launched the Internet as the public domain on August 6, 1991. This date is symbolic because the Internet was already functioning for almost a year as an internal domain in the CERN (the European Organization for Nuclear Research) near Geneva. Berners Lee wanted to standardize forms of electronic communication within the centre, and incidentally, opened at the same time, a new development in the history of social communication. The Internet over these 30 years has revolutionized social communication and all areas of human life. It has allowed people who previously could not speak to express their opinions, to actively influence discourse in the public sphere and even, in some cases, to reconfigure entire political systems in individual countries. In the following article I would like to take a look at how the information revolution has changed the forms of political communication and at least take a framework look at whether the Internet, has become a new opportunity for democratization of social life and to what extent it has happened.

Experimental Evaluation of Sub-Sampling IQ Detection for Low-Level RF Control in Particle Accelerator Systems

The low-level radio frequency (LLRF) control system is one of the fundamental parts of a particle accelerator, ensuring the stability of the electro-magnetic (EM) field inside the resonant cavities. It leverages on the precise measurement of the field by in-phase/quadrature (IQ) detection of an RF probe signal from the cavities, usually performed using analogue downconversion. This approach requires a local oscillator (LO) and is subject to hardware non-idealities like mixer nonlinearity and long-term temperature drifts. In this work, we experimentally evaluate IQ detection by direct sampling for the LLRF system of the Polish free electron laser (PolFEL) now under development at the National Centre for Nuclear Research (NCBJ) in Poland. We study the impact of the sampling scheme and of the clock phase noise for a 1.3-GHz input sub-sampled by a 400-MSa/s analogue-to-digital converter (ADC), estimating amplitude and phase stability below 0.01% and nearly 0.01°, respectively. The results are in line with state-of-the-art implementations, and demonstrate the feasibility of direct sampling for GHz-range LLRF systems.

MARIA Reactor Irradiation Technology Capabilities towards Advanced Applications

The MARIA research reactor is designed and operated as a multipurpose nuclear installation, combining material testing, neutron beam experiments, and medical and industrial radionuclide production, including molybdenum-99 (99Mo). Recently, after fuel conversion to LEU and rejuvenation of the staff while maintaining their experience, MARIA has been used to respond to the increased interest of the scientific community in advanced nuclear power studies, both fission and fusion. In this work, we would like to introduce MARIA’ s capabilities in the irradiation technology field and how it can serve future nuclear research worldwide.

Assessment of Radiation Shielding Properties of Polymer-Lead (II) Oxide Composites

Long term exposure to very high levels of radiations from medical diagnostic centres, industries, nuclear research establishments and nuclear weapon development have resulted in health effects such as cancer and acute radiation syndrome, hence the need for proper radiation shielding. This paper investigated Epoxy-Lead (II) Oxide (PbO) composite as radiation shielding. The composites were prepared by dispersion of microsized PbO particles into polymeric materials using effective melt-mixing method and cast in a 4 cm by 6 cm rectangular aluminium Mold with a thickness of 5 mm and was allowed to set over night at room temperature. The gamma ray attenuation ability of the composites were studied using gamma ray transmission or attenuation coefficient determination for the gamma ray energy. Three gamma ray sources Ba-133, Cs-137 and Co-60 were employed. The density, linear attenuation coefficient, half value layer (HVL), relaxation length and heaviness of the samples were determined. The measured values of linear attenuation coefficient increased with increasing filler concentration in all the samples at all gamma ray energies. It was also noticed that 40 % and 50 % filler samples attenuates more relative to the other samples under study. The maximum linear attenuation attained was found at energy of 662 keV. The composites have been found to possessed medical gamma-ray attenuation characteristics among the sample materials over a certain photon energy range (0.08 MeV–1.332 MeV) and found useful as a biological radiation shielding against gamma rays.

Hybrid quantum classical graph neural networks for particle track reconstruction

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.

Preliminary computational and experimental design studies of the ISHTAR thermostatic rig for the high-temperature reactors materials irradiation

The Irradiation System for High-Temperature Reactors (ISHTAR) thermostatic rig will be used to irradiate advanced core material samples in conditions corresponding to those prevailing in the high-temperature reactors (HTRs): these conditions include a stable temperature extending up to 1000°C in the helium atmosphere. Computational and experimental studies concerning the design have been conducted, proving the possibility of these conditions’ fulfillment inside the rig while maintaining the safety limits for MARIA research reactor. The outcome is the thermostatic rig design that will be implemented in the MARIA reactor. Appropriate irradiation temperature will be achieved by a combination of electric heating with the control system, gamma heating, and a helium insulation gap with precisely designed thickness. The ISHTAR rig will be placed inside the vertical irradiation channel, which is located in the reactor pool. The device is being developed from scratch at the Nuclear Facilities Operation Department of the National Centre for Nuclear Research as a part of the GOSPOSTRATEG programme.