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Open access

Richárd Forster and Fülöp Ágnes

Abstract

In the last centuries the experimental particle physics began to develop thank to growing capacity of computers among others. It is allowed to know the structure of the matter to level of quark gluon. Plasma in the strong interaction. Experimental evidences supported the theory to measure the predicted results. Since its inception the researchers are interested in the track reconstruction. We studied the jet browser model, which was developed for 4π calorimeter. This method works on the measurement data set, which contain the components of interaction points in the detector space and it allows to examine the trajectory reconstruction of the final state particles. We keep the total energy in constant values and it satisfies the Gauss law. Using GPUs the evaluation of the model can be drastically accelerated, as we were able to achieve up to 223 fold speedup compared to a CPU based parallel implementation.

Open access

Richárd Forster and Ágnes Fülöp

Abstract

The reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately. More granular parallelization of the kt cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The kt method allows to know the development of particles due to the collision of high-energy nucleus-nucleus. The hierarchical clustering algorithms works on graphs, so the particle information used by the standard kt algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit's standard longitudinal invariant kt implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be 1:6 times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a 12 times faster computing performance, also being scalable enough to efficiently run on GPUs.

Open access

Richárd Forster and Agnes Fülöp

Abstract

Following up on our previous study on applying hierarchical clustering algorithms to high energy particle physics, this paper explores the possibilities to use deep learning to generate models capable of processing the clusterization themselves. The technique chosen for training is reinforcement learning, that allows the system to evolve based on interactions between the model and the underlying graph. The result is a model, that by learning on a modest dataset of 10, 000 nodes during 70 epochs can reach 83, 77% precision for hierarchical and 86, 33% for high energy jet physics datasets in predicting the appropriate clusters.

Open access

Richárd Forster and Ágnes Fülöp

Abstract

The Yang-Mills fields plays important role in the strong interaction, which describes the quark gluon plasma. The non-Abelian gauge theory provides the theoretical background understanding of this topic. The real time evolution of the classical fields is derived by the Hamiltonian for SU(2) gauge field tensor. The microcanonical equations of motion is solved on 3 dimensional lattice and chaotic dynamics was searched by the monodromy matrix. The entropy-energy relation was presented by Kolmogorov-Sinai entropy. We used block Hessenberg reduction to compute the eigenvalues of the current matrix. While the purely CPU based algorithm can handle effectively only a small amount of values, the GPUs provide enough performance to give more computing power to solve the problem.

Open access

Richárd Forster and Ágnes Fülöp

Abstract

The Yang-Mills fields have an important role in the non- Abelian gauge field theory which describes the properties of the quarkgluon plasma. The real time evolution of the classical fields is given by the equations of motion which are derived from the Hamiltonians to contain the term of the SU(2) gauge field tensor. The dynamics of the classical lattice Yang-Mills equations are studied on a 3 dimensional regular lattice. During the solution of this system we keep the total energy on constant values and it satisfies the Gauss law. The physical quantities are desired to be calculated in the thermodynamic limit. The broadly available computers can handle only a small amount of values, while the GPUs provide enough performance to reach out for higher volumes of lattice vertices which approximates the aforementioned limit.

Open access

Parallel kt jet clustering algorithm

Dedicated to the memory of Antal Iványi

Richárd Forster and Ágnes Fűlőp

Abstract

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.