1,795 research outputs found
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
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