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

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

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

Simulation of processes of electric power consumption for traction in conditions of changing the traffic schedule of freight trains at the electrified sections

The paper is devoted to the solution of the problem of increasing the energy efficiency of the transportation process on the electrified sections of the railways. The task is considered in the aspect of energy efficiency when comparing forecasted traffic schedules with each other and assessing the effectiveness of implementing the forecasted and executed schedule of train traffic at the section. The basis for the calculations is a simulation modeling of the interaction between the electric rolling stock and the traction power supply system at the sections with different track profiles. Simulation modeling was carried out for the conditions of changing the traffic schedule of freight trains and maintaining the amount of traffic and the amount of work unchanged. The results of the change in the amount of electric power for traction and the level of unbalance of energy for existing sections of constant and alternating current are used as the basis for construction of approximating models, in the function of which regression and neural network models are used. Comparison of the results of approximation of the considered models for the estimation of changes in amount of electric power for traction and unbalance is made. Models with the best results of approximation to simulation results are determined

Improvement of the method of calculation of the parameters of the universal current collector with the increased motion speeds

The paper considers the main stages of development of a computer model of current collectors of an electric rolling stock. The original description of the kinematic scheme of a universal measuring current collector with the subsequent implementation of mathematical model in Matlab SimMechanics environment is offered. The model is based on the use of the method of homogeneous coordinates to represent the kinematic scheme of the current collector. It is shown that the computer model accurately and adequately describes the processes occurring with the current collector, is a universal instrument for simulating any types of current collectors, allows changing a wide range of parameters and characteristics incorporated in it

Technology for reducing the consumption and losses of electrical energy in the power supply systems of railway consumers

The paper is devoted to the development of technology for reducing the consumption and losses of electrical energy of railway transport enterprises on the basis of the concept of “smart enterprise”. A design solution to the monitoring and management system for energy efficiency indicators was proposed; monitored parameters and influencing factors were determined. The application of the simulation model in the MATLAB/Simulink program for determining the set values of the monitored system parameters, namely, power losses and power factor depending on the influencing factors, is considered. The approximating equation is obtained. Specific power losses were calculated for various control options

Technology for reducing the consumption and losses of electrical energy in the power supply systems of railway consumers

The paper is devoted to the development of technology for reducing the consumption and losses of electrical energy of railway transport enterprises on the basis of the concept of “smart enterprise”. A design solution to the monitoring and management system for energy efficiency indicators was proposed; monitored parameters and influencing factors were determined. The application of the simulation model in the MATLAB/Simulink program for determining the set values of the monitored system parameters, namely, power losses and power factor depending on the influencing factors, is considered. The approximating equation is obtained. Specific power losses were calculated for various control options

Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks

The problem with training spiking neural networks (SNNs) is relevant due to the ultra-low power consumption these networks could exhibit when implemented in neuromorphic hardware. The ongoing progress in the fabrication of memristors, a prospective basis for analogue synapses, gives relevance to studying the possibility of SNN learning on the base of synaptic plasticity models, obtained by fitting the experimental measurements of the memristor conductance change. The dynamics of memristor conductances is (necessarily) nonlinear, because conductance changes depend on the spike timings, which neurons emit in an all-or-none fashion. The ability to solve classification tasks was previously shown for spiking network models based on the bio-inspired local learning mechanism of spike-timing-dependent plasticity (STDP), as well as with the plasticity that models the conductance change of nanocomposite (NC) memristors. Input data were presented to the network encoded into the intensities of Poisson input spike sequences. This work considers another approach for encoding input data into input spike sequences presented to the network: temporal encoding, in which an input vector is transformed into relative timing of individual input spikes. Since temporal encoding uses fewer input spikes, the processing of each input vector by the network can be faster and more energy-efficient. The aim of the current work is to show the applicability of temporal encoding to training spiking networks with three synaptic plasticity models: STDP, NC memristor approximation, and PPX memristor approximation. We assess the accuracy of the proposed approach on several benchmark classification tasks: Fisher’s Iris, Wisconsin breast cancer, and the pole balancing task (CartPole). The accuracies achieved by SNN with memristor plasticity and conventional STDP are comparable and are on par with classic machine learning approaches

Determination of the number of ψ(3686)\psi(3686) events at BESIII

The numbers of ψ(3686) events accumulated by the BESIII detector for the data taken during 2009 and 2012 are determined to be and , respectively, by counting inclusive hadronic events, where the uncertainties are systematic and the statistical uncertainties are negligible. The number of events for the sample taken in 2009 is consistent with that of the previous measurement. The total number of ψ(3686) events for the two data taking periods is