搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

低横动量$\varUpsilon(1 S)$介子在强子气体中的分布

丁慧强 戴婷婷 程鸾 张卫宁 王恩科

引用本文:
Citation:

低横动量$\varUpsilon(1 S)$介子在强子气体中的分布

丁慧强, 戴婷婷, 程鸾, 张卫宁, 王恩科

Distribution of low-$p_{\rm{T}}$ $\varUpsilon(1 S)$ in hadron gas

Ding Hui-Qiang, Dai Ting-Ting, Cheng Luan, Zhang Wei-Ning, Wang En-Ke
PDF
HTML
导出引用
  • $ \varUpsilon(1S) $是高能重离子碰撞中的干净探针, 其研究具有重要意义. 强子气体中$ \varUpsilon(1S) $的分布受到集体流、量子效应和强相互作用三种效应的影响. 此前的模型均只分析了其中一到两种效应而忽略了其他效应, 将忽略的效应放入到了拟合参数中. 本文从新的视角提出了两体分形模型, 从物理计算而非参数拟合的方法开展研究. 本文认为$ \varUpsilon(1S) $与最相近的介子形成两体结构, 与介子内部的两夸克结构具有自相似性. 引入环境影响因子$q_{{\rm{fqs}}}$来描述上述三种效应影响; 引入伴随因子$ q_2 $来描述$ b, \bar{b} $之间的相互作用和上述三种效应的影响. 通过求解概率与熵方程, 得到不同碰撞条件下$q_{{\rm{fqs}}}$$ q_2 $的值. 将$q_{{\rm{fqs}}}$代入到分布函数中, 得到低横动量区的横动量谱并与实验对比, 结果表明理论与实验符合良好. 还分析了$q_{{\rm{fqs}}}$随温度的变化关系, 并发现$q_{{\rm{fqs}}}$ > 1, 这是由环境效应减少了$ \varUpsilon(1S) $的微观状态数所致. 另外, 计算显示$q_{{\rm{fqs}}}$随着系统温度的降低而减小, 这与随着系统膨胀强子气体影响减弱的现象相一致.
    The $ \varUpsilon(1S) $ meson serves as a reliable probe in heavy-ion collisions, as the regeneration process in the quark-gluon plasma (QGP) is negligible compared to $ J/\psi $. Therefore, the distribution of $ \varUpsilon(1S) $ in the hadron gas provides valuable information about the QGP. Consequently, its study holds great significance. The distribution in the hadron gas is influenced by flow, quantum, and strong interaction effects. Previous models have predominantly focused on one or two of these effects while neglecting the others, resulting in the inclusion of unconsidered effects in the fitted parameters. In this paper, we aim to comprehensively examine all three effects simultaneously from a novel fractal perspective through physical calculations, rather than relying solely on data fitting. Close to the critical temperature, the combined action of the three effects leads to the formation of a two-meson structure comprising $ \varUpsilon(1S) $ and its nearest neighboring meson. However, with the evolution of the system, most of these states undergo disintegration. To describe this physical process, we establish a two-particle fractal (TPF) model. Our model proposes that, under the influence of the three effects near the critical temperature, a self-similarity structure emerges, involving a $ \varUpsilon(1S) $-π two-meson state and a $ \varUpsilon(1S) $-π two-quark state. As the system evolves, the two-meson structure gradually disintegrates. We introduce an influencing factor, $ q_{{\rm{fqs}}} $, to account for the flow, quantum, and strong interaction effects, as well as an escort factor, $ q_2 $, to represent the binding force between b and $ \bar{b} $ and the combined impact of the three effects. By solving the probability and entropy equations, we derive the values of $ q_{{\rm{fqs}}} $ and $ q_2 $ at various collision energies. Substituting the value of $ q_{{\rm{fqs}}} $ into the distribution function, we successfully obtain the transverse momentum spectrum of low-$ p_{\rm{T}} $ $ \varUpsilon(1S) $, which demonstrates good agreement with experimental data. Additionally, we analyze the evolution of $ q_{{\rm{fqs}}} $ with temperature. Interestingly, we observe that $ q_{{\rm{fqs}}} $ is greater than 1 and decreases as the temperature decreases. This behavior arises from the fact that the three effects reduce the number of microstates, leading to $ q_{{\rm{fqs}}}>1 $. The decrease in $ q_{{\rm{fqs}}} $ with system evolution aligns with the understanding that the influence of the three effects diminishes as the system expands. In the future, the TPF model can be employed to investigate other mesons and resonance states.
      通信作者: 程鸾, luancheng@dlut.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 12175031)和广东省核物质科学与技术重点实验室(批准号: 2019B121203010)资助的课题.
      Corresponding author: Cheng Luan, luancheng@dlut.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 12175031) and the Key Laboratory of Nuclear Science of Guangdong Province, China (Grant No. 2019B121203010)
    [1]

    Lee K S, Heinz U, Schnedermann E 1990 Z. Phys. C: Part. Fields 48 525Google Scholar

    [2]

    Van Hove L 1982 Phys. Lett. B 118 138Google Scholar

    [3]

    Krämer M 2001 Prog. Part. Nucl. Phys. 47 141Google Scholar

    [4]

    Lansberg J P 2009 Eur. Phys. J. C 61 693Google Scholar

    [5]

    Brambilla N 2011 Eur. Phys. J. C 71 1534Google Scholar

    [6]

    Andronic A 2016 Eur. Phys. J. C 76 107Google Scholar

    [7]

    Mócsy Á, Petreczky P, Strickland M 2013 Int. J. Mod. Phys. A 28 1340012Google Scholar

    [8]

    Rapp R, Blaschke D, Crochet P 2010 Prog. Part. Nucl. Phys. 65 209Google Scholar

    [9]

    Rothkopf A 2020 Phys. Rep. 858 1Google Scholar

    [10]

    Zhao J, Zhou K, Chen S, Zhuang P 2020 Prog. Part. Nucl. Phys. 114 103801Google Scholar

    [11]

    Mócsy Á 2009 Eur. Phys. J. C 61 705Google Scholar

    [12]

    Karsch F, Mehr M T, Satz H 1988 Z. Phys. C: Part. Fields 37 617Google Scholar

    [13]

    Guo Y, Dong L, Pan J, Moldes M R 2019 Phys. Rev. D 100 036011Google Scholar

    [14]

    Kogut J B 1983 Rev. Mod. Phys. 55 775Google Scholar

    [15]

    Digal S, Petreczky P, Satz H 2001 Phys. Rev. D 64 094015Google Scholar

    [16]

    Burnier Y, Rothkopf A 2017 Phys. Rev. D 95 054511Google Scholar

    [17]

    Young C, Dusling K 2013 Phys. Rev. C 87 065206Google Scholar

    [18]

    Akamatsu Y, Rothkopf A 2012 Phys. Rev. D 85 105011Google Scholar

    [19]

    Zhou K, Xu N, Zhuang P 2014 Nucl. Phys. A 931 654Google Scholar

    [20]

    Herrmann N, Wessels J P, Wienold T 1999 Annu. Rev. Nucl. Part. Sci. 49 581Google Scholar

    [21]

    Schnedermann E, Sollfrank J, Heinz U 1993 Phys. Rev. C 48 2462Google Scholar

    [22]

    Wong C Y 2002 Phys. Rev. C 65 034902Google Scholar

    [23]

    Lin Z, Ko C M 2001 Phys. Lett. B 503 104Google Scholar

    [24]

    Abreu L M, Navarra F S, Nielsen M 2020 Phys. Rev. C 101 014906Google Scholar

    [25]

    Tang Z, Xu Y, Ruan L, van Buren G, Wang F, Xu Z 2009 Phys. Rev. C 79 051901Google Scholar

    [26]

    Reygers K, Schmah A, Berdnikova A, Sun X 2020 Phys. Rev. C 101 064905Google Scholar

    [27]

    Cleymans J, Satz H 1993 Z. Phys. C: Part. Fields 57 135Google Scholar

    [28]

    Andronic A, Braun-Munzinger P, Redlich K, Stachel J 2007 Nucl. Phys. A 789 334Google Scholar

    [29]

    Mandelbrot B 1967 Science 156 636Google Scholar

    [30]

    Li B A, Ko C M 1995 Phys. Rev. C 52 2037Google Scholar

    [31]

    Pathria R, Beale D P 2022 Formulation of Quantum Statistics (London:Elsevier) pp127–128

    [32]

    Mandelbrot B 1982 The Fractal Geometry of Nature (New York: W. H. Freeman) pp25–74

    [33]

    Dumitru A, Guo Y, Mócsy Á, Strickland M 2009 Phys. Rev. D 79 054019Google Scholar

    [34]

    Particle Data Group 2022 Prog. Theor. Exp. Phys. 2022 083C01Google Scholar

    [35]

    Srivastava P K, Chaturvedi O S K, Thakur L 2018 Eur. Phys. J. C 78 440Google Scholar

    [36]

    Crater H W, Yoon J H, Wong C Y 2009 Phys. Rev. D 79 034011Google Scholar

    [37]

    Ristea C 2018 Eur. Phys. J. Web Conf. 191 01004Google Scholar

    [38]

    Cè M, Harris T, Meyer H B, Toniato A, Török C 2021 J. High Energy Phys. 12 215Google Scholar

    [39]

    Beck C, Schögl F 1995 Thermodynamics of Chaotic Systems (Cambridge: Cambridge University Press) pp88–127

    [40]

    Tél T 1988 Z. Naturforsch., A: Phys. Sci. 43 1154Google Scholar

    [41]

    Schroeder M 2009 Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise (New York: W. H. Freeman and Company) pp103–121

    [42]

    Abe S, Okamoto Y 2001 Nonextensive Statistical Mechanics and Its Applications (Berlin: Springer) pp5–6

    [43]

    Tsallis C 1988 J. Stat. Phys. 52 479Google Scholar

    [44]

    Cleymans J, Worku D 2012 Eur. Phys. J. A 48 160Google Scholar

    [45]

    Beck C 2000 Physica A 286 164Google Scholar

    [46]

    CMS Collaboration 2017 Phys. Lett. B 770 357Google Scholar

    [47]

    CMS Collaboration 2019 Phys. Lett. B 790 270Google Scholar

  • 图 1  强子气体中b夸克和$ \bar{b} $反夸克在不同层次的自相似结构 (a)真空中的自由$ \varUpsilon(1 S) $介子; (b)介子层次强子气体中的$ \varUpsilon(1 S) $介子; (c)真空中的自由b夸克和$ \bar{b} $反夸克; (d)夸克层次强子气体中的b夸克和$ \bar{b} $反夸克

    Fig. 1.  Self-similarity structure of b quark and $ \bar{b} $ anti-quark in hadron gas: (a) Free $ \varUpsilon(1 S) $ in vacuum; (b) $ \varUpsilon(1 S) $ in hadron gas from meson aspect; (c) free b and $ \bar{b} $ in vacuum; (d) $ \varUpsilon(1 S) $ in hadron gas from quark aspect

    图 2  Pb-Pb在不同碰撞能量以及0—100%碰撞对心度下, 低横动量$ \varUpsilon(1 S) $介子在中心快度区间$ |y|<2.4 $的横动量谱. 实验数据来源于LHC[46,47]

    Fig. 2.  Transverse momentum spectrum of low-$ p_{\rm{T}}$ $ \varUpsilon(1 S) $ in Pb-Pb at different collision energies for 0–100% centrality, in mid-rapidity region $ |y| <2.4 $. The experimental data are taken from LHC[46,47]

    图 3  不同固定温度T下的强子气体影响因子$q_{{\rm{fqs}}}$

    Fig. 3.  Hadron gas influencing factor $q_{{\rm{fqs}}}$ at different fixed temperature T

    表 1  Pb-Pb在不同碰撞能量以及0—100%碰撞对心度下, 中心快度区$|y|<2.4$内的$\varUpsilon(1 S)$介子运动空间半径$r_0$的值

    Table 1.  In mid-rapidity region $|y|<2.4$, radius $r_0$ of $\varUpsilon(1 S)$ motion space under different collision energies and 0–100% centrality for Pb-Pb

    碰撞能量($\sqrt{s_{\rm{NN}}}$) 2.76/TeV 5.02/TeV
    $r_0$/fm 2.92 3.20
    下载: 导出CSV

    表 2  Pb-Pb在不同碰撞能量以及0—100%碰撞对心度下, 中心快度区$|y|<2.4$内影响因子$q_{{\rm{fqs}}}$$q_2$的数值

    Table 2.  In mid-rapidity region $|y|<2.4$, values of $q_{{\rm{fqs}}}$ and $q_2$ for Pb-Pb in 0–100% centrality at different collision energies

    碰撞能量 ($\sqrt{s_{\rm{NN}}}$ ) 2.76/TeV 5.02/TeV
    $q_{{\rm{fqs}}}$ 1.0732 1.1051
    $q_{2}$ 1.4249 1.4144
    下载: 导出CSV
    Baidu
  • [1]

    Lee K S, Heinz U, Schnedermann E 1990 Z. Phys. C: Part. Fields 48 525Google Scholar

    [2]

    Van Hove L 1982 Phys. Lett. B 118 138Google Scholar

    [3]

    Krämer M 2001 Prog. Part. Nucl. Phys. 47 141Google Scholar

    [4]

    Lansberg J P 2009 Eur. Phys. J. C 61 693Google Scholar

    [5]

    Brambilla N 2011 Eur. Phys. J. C 71 1534Google Scholar

    [6]

    Andronic A 2016 Eur. Phys. J. C 76 107Google Scholar

    [7]

    Mócsy Á, Petreczky P, Strickland M 2013 Int. J. Mod. Phys. A 28 1340012Google Scholar

    [8]

    Rapp R, Blaschke D, Crochet P 2010 Prog. Part. Nucl. Phys. 65 209Google Scholar

    [9]

    Rothkopf A 2020 Phys. Rep. 858 1Google Scholar

    [10]

    Zhao J, Zhou K, Chen S, Zhuang P 2020 Prog. Part. Nucl. Phys. 114 103801Google Scholar

    [11]

    Mócsy Á 2009 Eur. Phys. J. C 61 705Google Scholar

    [12]

    Karsch F, Mehr M T, Satz H 1988 Z. Phys. C: Part. Fields 37 617Google Scholar

    [13]

    Guo Y, Dong L, Pan J, Moldes M R 2019 Phys. Rev. D 100 036011Google Scholar

    [14]

    Kogut J B 1983 Rev. Mod. Phys. 55 775Google Scholar

    [15]

    Digal S, Petreczky P, Satz H 2001 Phys. Rev. D 64 094015Google Scholar

    [16]

    Burnier Y, Rothkopf A 2017 Phys. Rev. D 95 054511Google Scholar

    [17]

    Young C, Dusling K 2013 Phys. Rev. C 87 065206Google Scholar

    [18]

    Akamatsu Y, Rothkopf A 2012 Phys. Rev. D 85 105011Google Scholar

    [19]

    Zhou K, Xu N, Zhuang P 2014 Nucl. Phys. A 931 654Google Scholar

    [20]

    Herrmann N, Wessels J P, Wienold T 1999 Annu. Rev. Nucl. Part. Sci. 49 581Google Scholar

    [21]

    Schnedermann E, Sollfrank J, Heinz U 1993 Phys. Rev. C 48 2462Google Scholar

    [22]

    Wong C Y 2002 Phys. Rev. C 65 034902Google Scholar

    [23]

    Lin Z, Ko C M 2001 Phys. Lett. B 503 104Google Scholar

    [24]

    Abreu L M, Navarra F S, Nielsen M 2020 Phys. Rev. C 101 014906Google Scholar

    [25]

    Tang Z, Xu Y, Ruan L, van Buren G, Wang F, Xu Z 2009 Phys. Rev. C 79 051901Google Scholar

    [26]

    Reygers K, Schmah A, Berdnikova A, Sun X 2020 Phys. Rev. C 101 064905Google Scholar

    [27]

    Cleymans J, Satz H 1993 Z. Phys. C: Part. Fields 57 135Google Scholar

    [28]

    Andronic A, Braun-Munzinger P, Redlich K, Stachel J 2007 Nucl. Phys. A 789 334Google Scholar

    [29]

    Mandelbrot B 1967 Science 156 636Google Scholar

    [30]

    Li B A, Ko C M 1995 Phys. Rev. C 52 2037Google Scholar

    [31]

    Pathria R, Beale D P 2022 Formulation of Quantum Statistics (London:Elsevier) pp127–128

    [32]

    Mandelbrot B 1982 The Fractal Geometry of Nature (New York: W. H. Freeman) pp25–74

    [33]

    Dumitru A, Guo Y, Mócsy Á, Strickland M 2009 Phys. Rev. D 79 054019Google Scholar

    [34]

    Particle Data Group 2022 Prog. Theor. Exp. Phys. 2022 083C01Google Scholar

    [35]

    Srivastava P K, Chaturvedi O S K, Thakur L 2018 Eur. Phys. J. C 78 440Google Scholar

    [36]

    Crater H W, Yoon J H, Wong C Y 2009 Phys. Rev. D 79 034011Google Scholar

    [37]

    Ristea C 2018 Eur. Phys. J. Web Conf. 191 01004Google Scholar

    [38]

    Cè M, Harris T, Meyer H B, Toniato A, Török C 2021 J. High Energy Phys. 12 215Google Scholar

    [39]

    Beck C, Schögl F 1995 Thermodynamics of Chaotic Systems (Cambridge: Cambridge University Press) pp88–127

    [40]

    Tél T 1988 Z. Naturforsch., A: Phys. Sci. 43 1154Google Scholar

    [41]

    Schroeder M 2009 Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise (New York: W. H. Freeman and Company) pp103–121

    [42]

    Abe S, Okamoto Y 2001 Nonextensive Statistical Mechanics and Its Applications (Berlin: Springer) pp5–6

    [43]

    Tsallis C 1988 J. Stat. Phys. 52 479Google Scholar

    [44]

    Cleymans J, Worku D 2012 Eur. Phys. J. A 48 160Google Scholar

    [45]

    Beck C 2000 Physica A 286 164Google Scholar

    [46]

    CMS Collaboration 2017 Phys. Lett. B 770 357Google Scholar

    [47]

    CMS Collaboration 2019 Phys. Lett. B 790 270Google Scholar

  • [1] 赵大帅, 孙志, 孙兴, 孙怀得, 韩柏. 基于分形理论的微间隙空气放电.  , 2021, 70(20): 205207. doi: 10.7498/aps.70.20210362
    [2] 杨松青, 蒋沅, 童天驰, 严玉为, 淦各升. 基于Tsallis熵的复杂网络节点重要性评估方法.  , 2021, 70(21): 216401. doi: 10.7498/aps.70.20210979
    [3] 奚彩萍, 张淑宁, 熊刚, 赵惠昌. 多重分形降趋波动分析法和移动平均法的分形谱算法对比分析.  , 2015, 64(13): 136403. doi: 10.7498/aps.64.136403
    [4] 黄晓林, 霍铖宇, 司峻峰, 刘红星. 等概率符号化样本熵应用于脑电分析.  , 2014, 63(10): 100503. doi: 10.7498/aps.63.100503
    [5] 孙柯岩, 赵小莹, 张功磊, 臧洪明. 基于分形理论的飞机雷击初始附着点的数值模拟.  , 2014, 63(2): 029204. doi: 10.7498/aps.63.029204
    [6] 韩小静, 王音, 林正喆, 张文献, 庄军, 宁西京. 团簇异构体生长概率的理论预测.  , 2010, 59(5): 3445-3449. doi: 10.7498/aps.59.3445
    [7] 盛峥, 黄思训. 变分伴随正则化方法从雷达回波反演海洋波导(Ⅱ):实际反演试验.  , 2010, 59(6): 3912-3916. doi: 10.7498/aps.59.3912
    [8] 盛峥, 黄思训. 变分伴随正则化方法从雷达回波反演海洋波导(Ⅰ):理论推导部分.  , 2010, 59(3): 1734-1739. doi: 10.7498/aps.59.1734
    [9] 唐英干, 邸秋艳, 赵立兴, 关新平, 刘福才. 基于二维最小Tsallis交叉熵的图像阈值分割方法.  , 2009, 58(1): 9-15. doi: 10.7498/aps.58.9
    [10] 张 磊, 徐 耀, 蒋晓东, 梁丽萍, 吕海滨, 李绪平, 魏晓峰, 吴 东, 孙予罕. 多重分形谱研究溶胶-凝胶SiO2疏水减反膜的激光预处理作用.  , 2007, 56(7): 3833-3838. doi: 10.7498/aps.56.3833
    [11] 杨俊岭, 郭立新, 万建伟. 基于未充分发展海谱的分形海面模型及其电磁散射研究.  , 2007, 56(4): 2106-2114. doi: 10.7498/aps.56.2106
    [12] 丁国华, 祖方遒, 李先芬, 席 赟, 沈融融. 液态In-80wt%Sn合金结构的分形分析.  , 2006, 55(8): 4188-4192. doi: 10.7498/aps.55.4188
    [13] 郭立新, 王运华, 吴振森. 双尺度动态分形粗糙海面的电磁散射及多普勒谱研究.  , 2005, 54(1): 96-101. doi: 10.7498/aps.54.96
    [14] 邵元智, 钟伟荣, 任 山, 蔡志苏, 龚 雷. 纳米团聚生长的多重分形谱.  , 2005, 54(7): 3290-3296. doi: 10.7498/aps.54.3290
    [15] 牟致栋, 魏琦瑛. CuⅩⅧ离子谱线波长和跃迁概率的理论计算.  , 2005, 54(6): 2614-2619. doi: 10.7498/aps.54.2614
    [16] 李中新, 金亚秋. 双网格前后向迭代与谱积分法计算分形粗糙面的双站散射与透射.  , 2002, 51(7): 1403-1411. doi: 10.7498/aps.51.1403
    [17] 于会生, 孙霞, 罗守福, 王永瑞, 吴自勤. 非晶Ni-Cu-P合金化学沉积过程的多重分形谱研究.  , 2002, 51(5): 999-1003. doi: 10.7498/aps.51.999
    [18] 孙 霞, 熊 刚, 傅竹西, 吴自勤. ZnO薄膜原子力显微镜图像的多重分形谱.  , 2000, 49(5): 854-862. doi: 10.7498/aps.49.854
    [19] 王晓平, 周 翔, 何 钧, 廖良生, 吴自勤. 有机薄膜电致发光失效过程的多重分形谱研究.  , 1999, 48(10): 1911-1916. doi: 10.7498/aps.48.1911
    [20] 王利民. Vicsek分形上电子的能谱.  , 1997, 46(7): 1380-1387. doi: 10.7498/aps.46.1380
计量
  • 文章访问数:  2221
  • PDF下载量:  60
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-06-15
  • 修回日期:  2023-07-09
  • 上网日期:  2023-09-05
  • 刊出日期:  2023-10-05

/

返回文章
返回
Baidu
map