Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks

High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adversarial Network - a deep learning technique allowing for implicit estimation of the population distribution for a given set of objects. This approach lets us learn and then sample from the distribution of raw detector responses, conditioned on the parameters of the charged particle tracks. To evaluate the quality of the proposed model, we integrate a prototype into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator, with a speed-up of at least an order of magnitude. The prototype is trained on the responses from the inner part of the detector and, once expanded to the full detector, should be ready for use in physics tasks.Comment: This is a post-peer-review, pre-copyedit version of an article published in Eur. Phys. J. C. The final authenticated version is available online at: http://dx.doi.org/10.1140/epjc/s10052-021-09366-

Clark formula for local time for one class of Gaussian processes

In the article we present chaotic decomposition and analog of the Clark formula for the local time of Gaussian integrators. Since the integral with respect to Gaussian integrator is understood in Skorokhod sense, then there exist more than one Clark representation for the local time. We present different representations and discuss the representation with the minimal L_2-norm

A two-level Structural Equation Model for evaluating the external effectiveness of PhD

In recent years the number of PhDs in Italy has significantly grown and purposes of PhD courses have expanded from the traditional ones. The analysis of the contribution of PhD title for job placement and employment condition of PhDs is an important tool for evaluating the quality and the effectiveness of PhD courses. For this reason, knowledge of the employment status and career of PhDs becomes essential and can help to reduce the gap between academia and labour market. The aim of this paper is to estimate a two-level structural equation model with latent variables to assess the external effectiveness of PhD. The analysis is performed using data from the research "Current situation and employment prospects of PhDs", commissioned by National Committee for the Evaluation of the University System (CNVSU) to the Department of Statistics "G. Parenti" of the University of Florence. The proposed measure of "external effectiveness" is a latent variable obtained by evaluating the level of satisfaction with the employment status of PhDs who achieved the title in 2008. The opinion was expressed one year after obtaining PhD on a ten ordered point scale. External effectiveness indicators used are Consistency with studies, Utilization of the acquired skills and Compliance with the cultural interests

Statistical analysis of high-speed jet flows

The spatiotemporal dynamics of pressure fluctuations of a turbulent jet flow is examined from the viewpoints of symbolic permutations theory and Kolmogorov-Smirnov statistics. The methods are applied to unveil hidden structures in the near-field of the two jets corresponding to the NASA SHJAR SP3 and SP7 experiments. Large Eddy Simulations (LES) are performed using the high-resolution Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) accelerated on Graphics Processing Units (GPUs). It is demonstrated that the decomposition of the LES pressure solutions into symbolic patterns of simpler temporal structure reveals the existence of some orderly structures in the jet flows. To separate the non-linear dynamics of the revealed structures from the linear part, the results based on the pressure signals obtained from LES are compared with the surrogate dataset constructed from the original data

Application of Genetic Programming and Artificial Neural Network Approaches for Reconstruction of Turbulent Jet Flow Fields

Two Machine Learning (ML) methods are considered for reconstruction of turbulet signals corresponding to the Large Eddy Simulation database obtained by application of the high-resolution CABARET method accelerated on GPU cards for flow solutions of NASA Small Hot Jet Acoustic Rig (SHJAR) jets. The first method is the Feedforward Neural Networks technique, which was successfully implemented for a turbulent flow over a plunging aerofoil in (Lui and Wolf, 2019). The second method is based on the application of Genetic Programming, which is well-known in optimisation research, but has not been applied for turbulent flow reconstruction before. The reconstruction of local flow velocity and pressure signals as well as timedependent principle coefficients of the Spectral Proper Orthogonal Decomposition of turbulent pressure fluctuations are considered. Stability and dependency of the ML algorithms on the smoothness property and the sampling rate of the underlying turbulent flow signals are discussed

e+e- Pairs: a clock and a thermometer of heavy ion collisions

Recently, there is growing evidence that a new state of matter is formed in sqrt(s_NN)= 200 GeV Au+Au collisions at RHIC: a strongly coupled Quark Gluon Plasma of partonic degrees of freedom which develops a collective motion. Dilepton spectra are not affected by strong interaction and can therefore probe the whole time evolution of the collision. Thus they may be sensitive to onset of deconfinement, chiral symmetry restoration, as well as the production of thermal photons. The PHENIX experiment measured the production of e+e- pairs in p+p and Au+Au collisions at sqrt(s_NN)= 200 GeV. An enhanced dilepton yield in the mass range 150<m_ee<750 MeV/c^2 is measured. The excess increases faster with centrality than the number of participating nucleons and is concentrated at p_T<1GeV/c. At higher p_T the excess below 300 MeV/c^2 has been related to an enhanced production of direct photons possibly of thermal origin.Comment: Proceedings of Quark Matter 2008, 8 pages, 7 figure