Entropy per Rapidity in Pb-Pb Central Collisions using Thermal and Artificial Neural Network (ANN) Models at LHC Energies

被引：2

作者：

Habashy, D. M. ^{[1
]}

El-Bakry, Mahmoud Y. ^{[1
]}

Scheinast, Werner ^{[2
]}

Hanafy, Mahmoud ^{[3
]}

机构：

[1] Ain Shams Univ, Fac Educ, Phys Dept, Cairo 11771, Egypt

[2] Joint Inst Nucl Res, Veksler & Baldin Lab High Energy Phys, Dubna 141980, Russia

[3] Benha Univ, Fac Sci, Phys Dept, Banha 13518, Egypt

来源：

CHINESE PHYSICS C | 2022年 / 46卷 / 07期

关键词：

HRG; Tsallis; ANN; RPropp; HEAVY-ION COLLISIONS; EVENT SELECTION; PP;

D O I：

10.1088/1674-1137/ac5f9d

中图分类号：

O57 [原子核物理学、高能物理学];

学科分类号：

070202 ;

摘要：

The entropy per rapidity dS/dy produced in central Pb-Pb ultra-relativistic nuclear collisions at LHC energies is calculated using experimentally identified particle spectra and source radii estimated from Hanbury Brown-Twiss (HBT) correlations for particles pi, k, p, Lambda, Omega, and (Sigma) over bar and pi, k, p, Lambda, and K-s(0) at root s = 2.76 and 5.02 TeV, respectively. An artificial neural network (ANN) simulation model is used to estimate the entropy per rapidity dS/dy at the considered energies. The simulation results are compared with equivalent experimental data, and a good agreement is achieved. A mathematical equation describing the experimental data is obtained. Extrapolation of the transverse momentum spectra at p(T) = 0 is required to calculate dS/dy, thus, we use two different fitting functions, the Tsallis distribution and hadron resonance gas (HRG) model. The success of the ANN model in describing the experimental measurements leads to the prediction of several spectra values for the mentioned particles, which may lead to further predictions in the absence of experiments.

引用

页数：14

共 44 条

[1] Centrality dependence of π, K, and p production in Pb-Pb collisions at √sNN=2.76 TeV [J].

Abelev, B. ;

Adam, J. ;

Adamova, D. ;

Adare, A. M. ;

Aggarwal, M. M. ;

Rinella, G. Aglieri ;

Agnello, M. ;

Agocs, A. G. ;

Agostinelli, A. ;

Ahammed, Z. ;

Ahmad, N. ;

Masoodi, A. Ahmad ;

Ahn, S. A. ;

Ahn, S. U. ;

Ajaz, M. ;

Akindinov, A. ;

Aleksandrov, D. ;

Alessandro, B. ;

Alici, A. ;

Alkin, A. ;

Almaraz Avina, E. ;

Alme, J. ;

Alt, T. ;

Altini, V. ;

Altinpinar, S. ;

Altsybeev, I. ;

Andrei, C. ;

Andronic, A. ;

Anguelov, V. ;

Anielski, J. ;

Anson, C. ;

Anticic, T. ;

Antinori, F. ;

Antonioli, P. ;

Aphecetche, L. ;

Appelshaeuser, H. ;

Arbor, N. ;

Arcelli, S. ;

Arend, A. ;

Armesto, N. ;

Arnaldi, R. ;

Aronsson, T. ;

Arsene, I. C. ;

Arslandok, M. ;

Asryan, A. ;

Augustinus, A. ;

Averbeck, R. ;

Awes, T. C. ;

Aysto, J. ;

Azmi, M. D. .

PHYSICAL REVIEW C, 2013, 88 (04)

[2]

Abelev B. B, 2014, Phys. Lett. B, V728, P216

[3]

Acharya S., 2020, Phys. Rev. C, V101, P044907

[4] One-dimensional pion, kaon, and proton femtoscopy in Pb-Pb collisions at √SNN=2.76 TeV [J].

Adam, J. ;

Adamova, D. ;

Aggarwal, M. M. ;

Rinella, G. Aglieri ;

Agnello, M. ;

Agrawal, N. ;

Ahammed, Z. ;

Ahn, S. U. ;

Aimo, I. ;

Aiola, S. ;

Ajaz, M. ;

Akindinov, A. ;

Alam, S. N. ;

Aleksandrov, D. ;

Alessandro, B. ;

Alexandre, D. ;

Alfaro Molina, R. ;

Alici, A. ;

Alkin, A. ;

Alme, J. ;

Alt, T. ;

Altinpinar, S. ;

Altsybeev, I. ;

Alves Garcia Prado, C. ;

Andrei, C. ;

Andronic, A. ;

Anguelov, V. ;

Anielski, J. ;

Anticic, T. ;

Antinori, F. ;

Antonioli, P. ;

Aphecetche, L. ;

Appelshaeuser, H. ;

Arcelli, S. ;

Armesto, N. ;

Arnaldi, R. ;

Arsene, I. C. ;

Arslandok, M. ;

Audurier, B. ;

Augustinus, A. ;

Averbeck, R. ;

Azmi, M. D. ;

Bach, M. ;

Badala, A. ;

Baek, Y. W. ;

Bagnasco, S. ;

Bailhache, R. ;

Bala, R. ;

Baldisseri, A. ;

Pedrosa, F. Baltasar Dos Santos .

PHYSICAL REVIEW C, 2015, 92 (05)

[5]

Adam J., 2016, Phys. Rev. C, V93, P024905, DOI [DOI 10.1103/PHYSREVC.101.024905, 10.1103/PhysRevC.93.024905]

[6] The electrical impedance, AC conductivity and dielectric properties of phenol red compound investigated and modeled by an artificial neural network [J].

Ali, H. A. M. ;

Habashy, D. M. .

COMMUNICATIONS IN THEORETICAL PHYSICS, 2020, 72 (10)

[7]

Alsmadi MKS, 2009, INT J COMPUT SCI NET, V9, P378

[8] KS0 and Λ Production in Pb-Pb Collisions at √sNN=2: 76 TeV [J].

Abelev, B. ;

Adam, J. ;

Adamova, D. ;

Adare, A. M. ;

Aggarwal, M. M. ;

Rinella, G. Aglieri ;

Agnello, M. ;

Agocs, A. G. ;

Agostinelli, A. ;

Ahammed, Z. ;

Ahmad, N. ;

Masoodi, A. Ahmad ;

Ahmed, I. ;

Ahn, S. U. ;

Ahn, S. A. ;

Aimo, I. ;

Aiola, S. ;

Ajaz, M. ;

Akindinov, A. ;

Aleksandrov, D. ;

Alessandro, B. ;

Alexandre, D. ;

Alici, A. ;

Alkin, A. ;

Alme, J. ;

Alt, T. ;

Altini, V. ;

Altinpinar, S. ;

Altsybeev, I. ;

Alves Garcia Prado, C. ;

Andrei, C. ;

Andronic, A. ;

Anguelov, V. ;

Anielski, J. ;

Anticic, T. ;

Antinori, F. ;

Antonioli, P. ;

Aphecetche, L. ;

Appelshaeuser, H. ;

Arbor, N. ;

Arcelli, S. ;

Armesto, N. ;

Arnaldi, R. ;

Aronsson, T. ;

Arsene, I. C. ;

Arslandok, M. ;

Augustinus, A. ;

Averbeck, R. ;

Awes, T. C. ;

Azmi, M. D. .

PHYSICAL REVIEW LETTERS, 2013, 111 (22)

[9] Artificial Neural Networks for Aging Simulation of Electrolysis Stacks [J].

Bahr, Matthias ;

Gusak, Andreas ;

Stypka, Sebastian ;

Oberschachtsiek, Bernd .

CHEMIE INGENIEUR TECHNIK, 2020, 92 (10) :1610-1617

[10] Artificial neural networks modeling of kinetic curves of celeriac (Apium graveolens L.) in vacuum drying [J].

Beigi, Mohsen ;

Ahmadi, Iman .

FOOD SCIENCE AND TECHNOLOGY, 2019, 39 :35-40

← 1 2 3 4 5 →