没有合适的资源?快使用搜索试试~ 我知道了~
首页介子核DY过程揭示parton横动量特性:NJL模型验证与非极化效应研究
本文探讨了介子核Drell-Yan (DY) 过程中的一个重要课题,即在纯核-核散射中非极化DY对的产生。研究主要聚焦于介子,特别是π介子,其内部的parton(基本粒子,如夸克和 gluon)的横向动量特性。作者利用核parton分布和文献中现有的核parton横向分布参数化来处理核方面的计算。这些分布是在Nambu-Jona-Lasinio (NJL) 模型框架下进行的,这个模型通过最小化过程确定尺度,该过程将基于NJL进化的π介子分布的一阶修正(NLO)预测与实验数据——快速差分DY横截面进行对比。 NJL模型是一种有效的低能量QCD理论,它在描述轻子和强相互作用方面提供了一种简洁的手段。在本文中,通过比较理论预测和实测数据,研究人员成功地确定了π介子的部分结构,尤其是其横向动量分布,直到2 GeV的水平。这一精度的计算结果表明了NJL模型在描述介子内部结构时的有效性。 文章的核心内容涉及对双链对的横向动量谱的详细分析,这直接影响到DY过程中的动量转移。在没有额外参数的情况下,仅依靠NJL模型得到的介子-核数据就能达到合理的描述,这进一步验证了该模型在处理这类物理现象时的实用性和预测能力。研究还着重讨论了介子非极化情况下,通过DY数据获取关于parton横向动量依赖部分分布行为的可能性,这对于理解基本粒子在强相互作用中的动态行为至关重要。 这篇研究不仅深化了我们对纯核-核散射过程中π介子内部结构的理解,而且提供了如何通过实验数据来测试和约束QCD理论模型的新视角。这项工作对于理论物理学家和实验物理学家来说都具有重要意义,因为它推动了我们对强相互作用性质的深入认识,并可能对未来相关实验设计和理论发展产生影响。
资源详情
资源推荐
Eur. Phys. J. C (2018) 78:644 Page 3 of 12 644
C
ab
(α
s
, z) = δ
ab
δ(1 − z) +
∞
n=1
α
s
2π
n
C
(n)
ab
(z). (8)
At present, the perturbative Sudakov form factor can be eval-
uated at next-to-next-to-leading logarithmic (NNLL) accu-
racy [11]. In the q ¯q annihilation channel pertinent to Drell–
Yan production, the evaluation of the Sudakov form factor at
next-to-leading logarithmic (NLL) accuracy, the one reached
in the present analysis, involves the coefficients
A
(1)
= 2C
F
B
(1)
=−3C
F
, (9)
which are the coefficient of the singular (1−z)
−1
and δ(1−z)
terms of the one-loop splitting function P
(0)
qq
(z) and
A
(2)
= KA
(1)
, K = C
A
67
18
−
π
2
6
− n
f
T
R
10
9
, (10)
which is the coefficient of the singular term of the two-loop
splitting function P
(1)
qq
(z) in the z → 1 limit [34]. The general
expression for C
(1)
ab
are given by [11,35]
C
(1)
qa
(z) = C
(1)
¯qb
(z)
= δ
qa
C
F
(1 − z) + δ
qa
δ(1 − z)C
F
−4 +
π
2
2
,
C
(1)
qg
(z) = C
(1)
¯qg
(z) = 2T
R
z(1 − z). (11)
Color factors in the previous equations are given by C
A
= 3,
C
F
= 4/3, T
R
= 1/2 with n
f
being the number of active
flavours. Together with the use of NLO pdfs, this guarantees
the evaluation of the cross section at small q
T
at NLL accu-
racy. The last ingredient in Eq. (2) is the non perturbative
form factor, S
h
1
h
2
NP
(b), which encodes the transverse struc-
ture of both the colliding hadrons. The latter is either fixed
by comparison with data or parametrized with the help of
hadronic models, as we shall do in this paper.
2.2 Proton structure
Predictions for the transverse momentum spectrum of DY
pairs produced in pion–proton collisions do rely on the
knowledge of the proton NP form factor. The latter is
extracted from the transverse momentum spectrum of DY
pairs produced in proton–proton (pp) and proton–nucleus
( pA) collisions. Quite recent analyses [15,16] have appeared
which address such an extraction. Since our aim here is to
establish the possibility of studying the pion transverse non
perturbative structure in pion–nucleus DY experiments, we
here intend to minimize the uncertainity coming from the
proton structure part of the calculation. We use the well
known and widely accepted results of Konychev and Nadol-
sky (KN05) [12] obtained within the CSS formalism [6]
where S
pp
NP
(b) is extracted from global fit to Z -boson and
low mass DY data, updating the results presented in Ref. [13].
The latter is parametrised as
S
pp
NP
(b) (12)
= exp{−[a
1
+ a
2
ln(M/(3.2GeV)) + a
3
ln(100x
1
x
2
)]b
2
}.
The a
i
parameters appearing in Eq. (12) are determined by a
minimisation procedure against data and are given by [12]
a
1
= 0.201 ±0.011, a
2
= 0.184 ±0.018,
a
3
=−0.026 ±0.007. (13)
The fit is fully specified once a prescription for the treatment
of the non perturbative, large-b, region both in the Sudakov
form factor, Eq. (6), and the parton distributions is given.
The authors of Ref. [12] adopt the so-called b
-prescription,
substituting b with
b
(b, b
max
) =
b
1 +
b
b
max
2
, (14)
and setting b
max
= 1.5GeV
−1
in the perturbative form fac-
tor. In principle, the same setting should be used in PDFs,
which are evaluated at the factorisation scale μ
F
= b
0
/b
∗
.
However this choice for b
max
may imply a call to a specific
PDFs parameterization below their lowest available scale,
Q
in
. Since in Ref. [12] cross sections are evaluated with
the NLO CTEQ6M PDFs [36], whose lowest Q accessible
is Q
in
= 1.3 GeV, the b
-prescription entering PDFs calls
is used with b
max
= b
0
/Q
in
0.86 GeV
−1
which always
guarantees μ
F
> Q
in
. It is important to remark that the non
perturbative form factor is determined not only by fitting the
parameters of the chosen functional form, but also by the
specific regularisation prescription and its associated param-
eters adopted to deal with the infrared region. In general all
these ingredients have been found to be highly correlated.
In order to present a benchmark of our code and to gauge
how theory performs in extrapolation regions, we compare
predictions from KN05 to the pA data of Ref. [37]. An addi-
tional ±25% normalisation error is assigned to the data [37].
In the original KN05 analysis, only the data at p
lab
= 400
GeV, q
T
< 1.4GeV,5< M/GeV < 9 were included in the
fit. In such a restricted region indeed the theory (solid lines)
performs well offering a good benchmark of our code, as
shown in the first row of Fig. 1. Since the π W data to be ana-
lyzed in the following are at p
lab
=252 GeV, it is important to
check how well the theory performs in extrapolation regions
at lower
√
s and higher DY rapidity. Therefore we present
in the second and third rows of Fig. 1 the KN05 benchmark
(dashed lines) versus data [37]at p
lab
= 200 and 300 GeV,
which were not included in the KN05 fit. By using Eq. (4)
and Eq. (5) and assuming the invariant mass values indicated
on the plots, the rapidity coverage of these data can be con-
123
剩余11页未读,继续阅读
weixin_38635682
- 粉丝: 0
- 资源: 968
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- JavaScript DOM事件处理实战示例
- 全新JDK 1.8.122版本安装包下载指南
- Python实现《点燃你温暖我》爱心代码指南
- 创新后轮驱动技术的电动三轮车介绍
- GPT系列:AI算法模型发展的终极方向?
- 3dsmax批量渲染技巧与VR5插件兼容性
- 3DsMAX破碎效果插件:打造逼真碎片动画
- 掌握最简GPT模型:Andrej Karpathy带你走进AI新时代
- 深入解析XGBOOST在回归预测中的应用
- 深度解析机器学习:原理、算法与应用
- 360智脑企业内测开启,探索人工智能新场景应用
- 3dsmax墙砖地砖插件应用与特性解析
- 微软GPT-4助力大模型指令微调与性能提升
- OpenSARUrban-1200:平衡类别数据集助力算法评估
- SQLAlchemy 1.4.39 版本特性分析与应用
- 高颜值简约个人简历模版分享
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功