We present a comprehensive study of the PELICAN machine learning algorithm
architecture in the context of both tagging (classification) and reconstructing
(regression) Lorentz-boosted top quarks, including the difficult task of
specifically identifying and measuring the $W$-boson inside the dense
environment of the boosted hadronic final state. PELICAN is a novel permutation
equivariant and Lorentz invariant or covariant aggregator network designed to
overcome common limitations found in architectures applied to particle physics
problems. Compared to many approaches that use non-specialized architectures
that neglect underlying physics principles and require very large numbers of
parameters, PELICAN employs a fundamentally symmetry group-based architecture
that demonstrates benefits in terms of reduced complexity, increased
interpretability, and raw performance. When tested on the standard task of
Lorentz-boosted top quark tagging, PELICAN outperforms existing competitors
with much lower model complexity and high sample efficiency. On the less common
and more complex task of four-momentum regression, PELICAN also outperforms
hand-crafted algorithms. We discuss the implications of symmetry-restricted
architectures for the wider field of machine learning for physics.

PELICAN 是一种新颖的置换等变和洛伦兹不变或协变的拼接网络，它在分类和回归洛伦兹增强顶夸克的上下文中，特别是在识别和测量密集环境中 $W$ 玻色子的困难任务中，相比其他方法表现出更少的复杂性、更高的可解释性和原始性能。

可解释的等变神经网络用于粒子物理学：PELICAN

Explainable Equivariant Neural Networks for Particle Physics: PELICAN

Based on the established task of identifying boosted, hadronically decaying
top quarks, we compare a wide range of modern machine learning approaches.
Unlike most established methods they rely on low-level input, for instance
calorimeter output. While their network architectures are vastly different,
their performance is comparatively similar. In general, we find that these new
approaches are extremely powerful and great fun.

该研究比较了一系列基于低层次输入的现代机器学习方法在鉴别被提升的轻子衰变的夸克的效果，结果表明这些新方法非常强大且有很高的效率。

顶级标签器的机器学习景观

The Machine Learning Landscape of Top Taggers

N-subjettiness is a jet shape designed to identify boosted hadronic objects
such as top quarks. Given N subjet axes within a jet, N-subjettiness sums the
angular distances of jet constituents to their nearest subjet axis. Here, we
generalize and improve on N-subjettiness by minimizing over all possible subjet
directions, using a new variant of the k-means clustering algorithm. On boosted
top benchmark samples from the BOOST2010 workshop, we demonstrate that a simple
cut on the 3-subjettiness to 2-subjettiness ratio yields 20% (50%) tagging
efficiency for a 0.23% (4.1%) fake rate, making N-subjettiness a highly
effective boosted top tagger. N-subjettiness can be modified by adjusting an
angular weighting exponent, and we find that the jet broadening measure is
preferred for boosted top searches. We also explore multivariate techniques,
and show that additional improvements are possible using a modified Fisher
discriminant. Finally, we briefly mention how our minimization procedure can be
extended to the entire event, allowing the event shape N-jettiness to act as a
fixed N cone jet algorithm.

本文通过使用 k-means 聚类算法的一种新变体来最小化所有可能的子喷口方向，对 N-subjettiness 进行了推广和改进，使 N-subjettiness 成为一种高效的强子双顶夸克的标记器，并通过多元技术展示了附加改进。

通过最小化 N-subjettiness 最大化 Boosted Top 鉴定

Maximizing Boosted Top Identification by Minimizing N-subjettiness

Higgs bosons which decay principally to top quarks, such as in the minimal
supersymmetric model, produce a peak-dip structure in the $gg\to t\bar t$
invariant-mass spectrum. This structure is potentially observable at the CERN
Large Hadron Collider. (see BNL theory home page
this http URL for recent preprints)

研究表明，Higgs 玻色子主要衰变为顶夸克，例如在最小超对称模型中，会在 $ gg	o tar t $ 不变质量谱中产生峰 - 陷结构。该结构在 CERN 大型强子对撞机上有可能被观测到。