Invertible Neural Networks (INN) have become established tools for the
simulation and generation of highly complex data. We propose a quantum-gate
algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the
LHC data of jet-associated production of a Z-boson that decays into leptons, a
standard candle process for particle collider precision measurements. We
compare the QINN's performance for different loss functions and training
scenarios. For this task, we find that a hybrid QINN matches the performance of
a significantly larger purely classical INN in learning and generating complex
data.

我们提出了一种量子门算法，称为量子可逆神经网络（QINN），并将其应用于 LHC 数据中的粒子对撞机精密测量的标准参考过程 - 带有衰变到轻子的 Z - 玻色子的喷注相关产生。我们在不同损失函数和训练场景下比较了 QINN 的表现，并发现在此任务中，混合量子可逆神经网络与学习和生成复杂数据方面性能相当于一个显著更大的纯经典可逆神经网络。

可生成可逆的量子神经网络

Generative Invertible Quantum Neural Networks

Machine learning has played an important role in the analysis of high-energy
physics data for decades. The emergence of deep learning in 2012 allowed for
machine learning tools which could adeptly handle higher-dimensional and more
complex problems than previously feasible. This review is aimed at the reader
who is familiar with high energy physics but not machine learning. The
connections between machine learning and high energy physics data analysis are
explored, followed by an introduction to the core concepts of neural networks,
examples of the key results demonstrating the power of deep learning for
analysis of LHC data, and discussion of future prospects and concerns.

本文介绍机器学习和深度学习在高能物理数据分析中的应用，包括神经网络核心概念、LHC 数据分析的关键结果以及未来的前景和担忧。

深度学习及其在 LHC 物理中的应用

Deep Learning and its Application to LHC Physics

We present the first determination of parton distributions of the nucleon at
NLO and NNLO based on a global data set which includes LHC data: NNPDF2.3. Our
data set includes, besides the deep inelastic, Drell-Yan, gauge boson
production and jet data already used in previous global PDF determinations, all
the relevant LHC data for which experimental systematic uncertainties are
currently available: ATLAS and LHCb W and Z lepton rapidity distributions from
the 2010 run, CMS W electron asymmetry data from the 2011 run, and ATLAS
inclusive jet cross-sections from the 2010 run. We introduce an improved
implementation of the FastKernel method which allows us to fit to this extended
data set, and also to adopt a more effective minimization methodology. We
present the NNPDF2.3 PDF sets, and compare them to the NNPDF2.1 sets to assess
the impact of the LHC data. We find that all the LHC data are broadly
consistent with each other and with all the older data sets included in the
fit. We present predictions for various standard candle cross-sections, and
compare them to those obtained previously using NNPDF2.1, and specifically
discuss the impact of ATLAS electroweak data on the determination of the
strangeness fraction of the proton. We also present collider PDF sets,
constructed using only data from HERA, Tevatron and LHC, but find that this
data set is neither precise nor complete enough for a competitive PDF
determination.

利用包括 LHC 数据的全球数据集，我们首次确定了核子部分子分布的 NLO 和 NNLO，通过引入改进的 FastKernel 方法，并采用更有效的最小化方法，我们还展示了 NNPDF2.3 PDF 机组并比较它们与 NNPDF2.1 机组。

基于 LHC 数据的质子子结构函数分布

Parton distributions with LHC data

RooStats is a project to create advanced statistical tools required for the
analysis of LHC data, with emphasis on discoveries, confidence intervals, and
combined measurements. The idea is to provide the major statistical techniques
as a set of C++ classes with coherent interfaces, so that can be used on
arbitrary model and datasets in a common way. The classes are built on top of
the RooFit package, which provides functionality for easily creating
probability models, for analysis combinations and for digital publications of
the results. We will present in detail the design and the implementation of the
different statistical methods of RooStats. We will describe the various classes
for interval estimation and for hypothesis test depending on different
statistical techniques such as those based on the likelihood function, or on
frequentists or bayesian statistics. These methods can be applied in complex
problems, including cases with multiple parameters of interest and various
nuisance parameters.

RooStats 是一个旨在创建先进的统计工具来分析 LHC 数据的项目，强调发现、置信区间和组合测量，其提供了一组 C++ 类作为主要的统计技术，用于任意模型和数据集的分析，并以一种通用的方法进行访问。