The numerical simulation allows to study the high energy particle physics. It plays important of role in the reconstruction and analyze of these experimental and theoretical researches. This requires a computer background with a large capacity. Jet physics is an intensively researched area, where the factorization process can be solved by algorithmic solutions. We studied parallelization of the kt cluster algorithms. This method allows to know the development of particles due to the collision of highenergy nucleus-nucleus. The Alice offline library contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. Using this simulation we can generate input particles, that we can further analyzed by clustering them, reconstructing their jet structure. The FastJet toolkit is an efficient C++ implementation of the most widely used jet clustering algorithms, among them the kt clustering. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture a 1:6 times faster runtime was achieved, paving the way to drastic performance increase using many-core architectures.
The reconstruction and analyzation of high energy particle physics data is just as important as the analyzation of the structure in real world networks. In a previous study it was explored how hierarchical clustering algorithms can be combined with k t cluster algorithms to provide a more generic clusterization method. Building on that, this paper explores the possibilities to involve deep learning in the process of cluster computation, by applying reinforcement learning techniques. The result is a model, that by learning on a modest dataset of 10, 000 nodes during 70 epochs can reach 83, 77% precision in predicting the appropriate clusters.
The concept of the statistical complexity is studied to characterize the classical kicked top model which plays important role in the qbit systems and the chaotic properties of the entanglement. This allow us to understand this driven dynamical system by the probability distribution in phase space to make distinguish among the regular, random and structural complexity on finite simulation. We present the dependence of the kicked top and kicked rotor model through the strength excitation in the framework of statistical complexity.
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