V. V. Ivanov

Separation of Trend and Chaotic Components of Time Series and Estimation of Their Characteristics by Linear Splines

This paper considers the problem of separating the trend and the chaotic component of chaotic time series in the absence of information on the characteristics of the chaotic component. Such a problem arises in nuclear physics, biomedicine, and many other applied fields. The scheme has two stages. At the first stage, smoothing linear splines with different values of smoothing parameter are used to separate the “trend component.” At the second stage, the method of least squares is used to find the unknown variance σ2 of the noise component.

Fast reconstruction of muon trajectories in the MUCH detector of the CBM experiment

The FAIR accelerator complex currently under construction at GSI (Darmstadt, Germany) will house the CBM experiment to be run by a large international collaboration, including JINR, Dubna. One of its priorities is the investigation of charmonium formation in high-energy nucleus–nucleus collisions. Charmonium decays such as J/ψ → μ+μ- will be reconstructed in real time. We propose a fast algorithm for reconstructing the trajectories of muons from the J/ψ decays in the MUCH detector.

Selection criteria for J/ψ → μ+ μ− decays using MUCH detector in CBM experiment

The Compressed Baryonic Matter (CBM) experimental setup is currently being constructed at the Facility for Antiproton and Ion Research (FAIR) acceleration complex in GSI (Darmstadt, Germany) by an international collaboration that includes a team from JINR. One of the primary goals of this experiment is to study charmonium production in high-energy nuclear collisions. It is meant to track down decays such as J/ψ → μ+μ− in the online mode. The present paper gives criteria for the effective selection of signal events by using exclusively data on charged muons collected in Muon Chamber (MUCH) coordinate stations.

Reconstruction and selection of J/ψ → e+e− decays registered by CBM setup in 25 AGeV AuAu collisions

The problem of reconstructing and selecting J/ϕ → e+e− decays registered by the CBM setup in AuAu collisions at 25 AGeV beam energy is considered. The key task in this problem is fast and reliable electron-positron identification using the energy losses of charged particles in the Transition Radiation Detector (TRD). We consider two methods for solving this problem: an artificial neuron network (ANN) and a modified non-parametric goodness-of-fit ωnk criterion. Our analysis shows that both approaches give similar results for the J/ϕ → e+e− yield and the signal-to-background ratio. Compared with the ωnk criterion, the method based on ANN has a number of disadvantages which are discussed in detail. Taking into consideration the very simple software implementation of the ωnk algorithm, it can be used for J/ϕ → e+e− decays selection in a real-time experiment.

Development of the geometry database for the CBM experiment

The paper describes the current state of the Geometry Database (Geometry DB) for the CBM experiment. The main purpose of this database is to provide convenient tools for: (1) managing the geometry modules; (2) assembling various versions of the CBM setup as a combination of geometry modules and additional files. The CBM users of the Geometry DB may use both GUI (Graphical User Interface) and API (Application Programming Interface) tools for working with it.

Forecasting daily passenger traffic volumes in the Moscow metro

In this paper we have developed a methodology for the medium-term prediction of daily volumes of passenger traffic in the Moscow metro. It includes three options for the forecast: (1) based on artificial neural networks (ANNs), (2) singular-spectral analysis implemented in the Caterpillar–SSA package, and (3) a combination of the ANN and Caterpillar–SSA approaches. The methods and algorithms allow the mediumterm forecasting of passenger traffic flows in the Moscow metro with reasonable accuracy.

A Review of Metric Analysis Applications to the Problems of Interpolating, Filtering and Predicting the Values of Onevariable and Multivariable Functions

At present, metric analysis schemes are developed to solve the problems of interpolation, smoothing, extrapolation of multivariable functions and their use for many applied problems [1, 2, 3, 4, 5, 6, 7]. In contrast to classical methods and schemes and a majority of other ones [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 23], the metric analysis, like artificial neuron networks, allows reconstructing the studied function values at each specified point of the definition domain separately. The individual position of this point with respect to the ones, where the values of the function are defined, is taken into account. Here we present a review of the published papers on the metric analysis used to solve the above problems, including those under the conditions of uncertainty of the defined values of the studied function. We present recommendations on using the metric analysis schemes and demonstrate the efficiency of the metric analysis methods and schemes.

Forecasting the daily electricity consumption in the Moscow region using artificial neural networks

In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.

Selection criteria for J

The Compressed Baryonic Matter (CBM) experimental setup is currently being constructed at the Facility for Antiproton and Ion Research (FAIR) acceleration complex in GSI (Darmstadt, Germany) by an international collaboration that includes a team from JINR. One of the primary goals of this experiment is to study charmonium production in high-energy nuclear collisions. It is meant to track down decays such as J/ψ → μ+μ− in the online mode. The present paper gives criteria for the effective selection of signal events by using exclusively data on charged muons collected in Muon Chamber (MUCH) coordinate stations.

Kalman filter-based fast track reconstruction for charged particles in a Compressed Baryonic Matter experiment using parallel computing on a multicore server at the Laboratory of Information Technologies, Joint Institute for Nuclear Research

One of the main goals in the Compressed Baryonic Matter (CBM) experiment (GSI, Germany) is to find parameters of charged particle trajectories. An online full event reconstruction is planned to be carried out in this experiment, thus demanding fast algorithms be developed, which make the most of the capabilities of modern CPU and GPU architectures. This paper presents the results of an analysis of the Kalman filter-based track reconstruction for charged particles implemented by using various code parallelization methods. A multicore server located at the Laboratory of Information Technologies, Joint Institute for Nuclear Research (LIT JINR), with two CPU Intel Xeon X5660 processors and a GPU Nvidia GTX 480 video card is used.