Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Bíró, Gábor"'
We investigate the formation and evolution of hot systems comprising charmed and light hadrons using non-extensive thermodynamics. We analyze data from pp, p--Pb, and Pb--Pb collisions at center-of-mass energies ranging from $\sqrt{s_{\rm NN}} = 2.76
Externí odkaz:
http://arxiv.org/abs/2409.01085
Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Deep Learn
Externí odkaz:
http://arxiv.org/abs/2408.17130
The transverse momentum spectra and their multiplicity dependence serve as key tools for extracting parameters that can be compared with theoretical models. This comparison aims to establish the behaviour and nature of the system created in the colli
Externí odkaz:
http://arxiv.org/abs/2403.07512
Publikováno v:
J. Phys. G: Nucl. Part. Phys. 51 085103 (2024)
We use open charm production to estimate how far we can see back in time in high-energy hadron-hadron collisions. We analyze the transverse momentum distributions of the identified D mesons from pp, p--Pb and A--A collisions at the ALICE and STAR exp
Externí odkaz:
http://arxiv.org/abs/2401.14282
Autor:
Bíró, Gábor, Barnaföldi, Gergely Gábor
The scaling properties of the final state charged hadron and mean jet multiplicity distributions, calculated by deep residual neural network architectures with different complexities are presented. The parton-level input of the neural networks are ge
Externí odkaz:
http://arxiv.org/abs/2303.05422
High-energy physics (HEP) provides ever-growing amount of data. To analyse these, continuously-evolving computational power is required in parallel by extending the storage capacity. Such developments play key roles in the future of this field howeve
Externí odkaz:
http://arxiv.org/abs/2303.05296
The results of a Machine Learning-based method is presented here to investigate the scaling properties of the final state charged hadron and mean jet multiplicity distributions. Deep residual neural network architectures with different complexities a
Externí odkaz:
http://arxiv.org/abs/2210.10548
We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor and machine learning techniques are test
Externí odkaz:
http://arxiv.org/abs/2205.12731
Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer V
Externí odkaz:
http://arxiv.org/abs/2111.15655