LHC analyses directly comparing data and simulated events bear the danger of
using first-principle predictions only as a black-box part of event simulation.
We show how simulations, for instance, of detector effects can instead be
inverted using generative networks. This allows us to reconstruct parton level
information from measured events. Our results illustrate how, in general, fully
conditional generative networks can statistically invert Monte Carlo
simulations. As a technical by-product we show how a maximum mean discrepancy
loss can be staggered or cooled.

本文介绍如何使用生成网络逆向模拟探测器效应等过程产生的事件，从而统计上反演蒙特卡罗模拟。此外，还介绍了最大均值失配损失如何被阶段性地或抑制地应用。

如何使用 GAN 消除探测器效应

How to GAN away Detector Effects

We explore the metric space of jets using public collider data from the CMS
experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at
the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central
jets with transverse momentum above 375 GeV. To validate the performance of the
CMS detector in reconstructing the energy flow of jets, we compare the CMS Open
Data to corresponding simulated data samples for a variety of jet kinematic and
substructure observables. Even without detector unfolding, we find very good
agreement for track-based observables after using charged hadron subtraction to
mitigate the impact of pileup. We perform a range of novel analyses, using the
"energy mover's distance" (EMD) to measure the pairwise difference between jet
energy flows. The EMD allows us to quantify the impact of detector effects,
visualize the metric space of jets, extract correlation dimensions, and
identify the most and least typical jet configurations. To facilitate future
jet studies with CMS Open Data, we make our datasets and analysis code
available, amounting to around two gigabytes of distilled data and one hundred
gigabytes of simulation files.

本文使用 CMS 探测器公开的对撞机数据探索喷注的度量空间，使用 “能流移动距离”（EMD）来衡量喷注能流的成对差异，量化探测器效应，提取相关维度，并确定最典型和最不典型的喷注配置。

用 CMS 开放数据探索喷注空间

Exploring the Space of Jets with CMS Open Data

When are two collider events similar? Despite the simplicity and generality
of this question, there is no established notion of the distance between two
events. To address this question, we develop a metric for the space of collider
events based on the earth mover's distance: the "work" required to rearrange
the radiation pattern of one event into another. We expose interesting
connections between this metric and the structure of infrared- and
collinear-safe observables, providing a novel technique to quantify event
modifications due to hadronization, pileup, and detector effects. We showcase
how this metrization unlocks powerful new tools for analyzing and visualizing
collider data without relying upon a choice of observables. More broadly, this
framework paves the way for data-driven collider phenomenology without
specialized observables or machine learning models.

为解决如何衡量加速器事例之间的距离，本文基于地球移动距离，构建了一种表示加速器事件的度量标准，发现这种度量标准与红外安全性和相干安全性可观测量的结构存在有趣的联系，可应用于研究由强子化、堆积和探测器效应引起的加速器事件变化，为无特定可观测量或机器学习模型的数据驱动型加速器现象学分析铺平了道路。