Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving the performance of deep learning in many applications, Lorentz group equivariance - a fundamental spacetime symmetry for elementary particles - has recently been incorporated into a deep learning model for jet tagging. However, the design is computationally costly due to the analytic construction of high-order tensors. In this article, we introduce LorentzNet, a new symmetry-preserving deep learning model for jet tagging. The message passing of LorentzNet relies on an efficient Minkowski dot product attention. Experiments on two representative jet tagging benchmarks show that LorentzNet achieves the best tagging performance and improves significantly over existing state-of-the-art algorithms. The preservation of Lorentz symmetry also greatly improves the efficiency and generalization power of the model, allowing LorentzNet to reach highly competitive performance when trained on only a few thousand jets. Code and models are available at \url{https://github.com/sdogsq/LorentzNet-release}.
Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety. We develop a variant of the Energy Flow Network architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. We derive conditions under which infrared and collinear safety can be maintained, and study the performance of these n…
Economical Jet Taggers -- Equivariant, Slim, and Quantized
Institut für Theoretische Physik, Universität Heidelberg（海德堡大学理论物理研究所）；Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg（海德堡大学跨学科科学计算中心）；Institute for Particle Physics Phenomenology, Durham University（杜伦大学粒子物理现象学研究所）
We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.
UCLouvain（瓦隆天主教大学）；Universität Heidelberg（海德堡大学）；Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University（上海交大李政道研究所与物理与天文学院）；Shanghai Jiao Tong University（上海交通大学）；Durham University（杜伦大学）
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Princeton University（普林斯顿大学）；University of California Berkeley（加州大学伯克利分校）
Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics have not born this out. We present the novel architecture VecNet that combines both symmetry-respecting and unconstrained operations to study and tune the degree of physics-informed GNNs. We introduce a novel metric, the \textit{ant factor}, to quantify the resource-efficiency of each configuration in the search-space. We find that a generalized ar…
Université catholique de Louvain（天主教鲁汶大学）；Universität Heidelberg（海德堡大学）；Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg（海德堡大学跨学科科学计算中心）；CERN（欧洲核子研究组织）
University of Chicago（芝加哥大学）；Enrico Fermi Institute（恩里科·费米研究所）
Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry, and is fully permutation-equivariant throughout. We study the application of this network architecture to the standard task of top quark tagging and show that the resulting network outperforms all existing c…
University of Chicago（芝加哥大学）；Atomwise（Atomwise公司）；Enrico Fermi Institute（恩里科·费米研究所）；Flatiron Institute（熨斗研究所）
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involves the tensor product. For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters and are much more physically interpretable than leading approaches that use C…
We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics. The particle convolution layer can be viewed as an extension of Deep Sets and Energy Flow network architectures, in which the permutation-invariant operator is promoted to a group convolution. While the PCN can be implemented for various kinds of symmetries, we consider the specific case of rotation about the jet axis the $η- ϕ$ plane. In two standard benchmark tasks, q/g tagging and top tagging, we show that the rotational PCN (rPCN) achieves performance …
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
University of Florida（佛罗里达大学）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；Indian Institute of Technology Bhilai（印度比莱印度理工学院）；University of Kansas（堪萨斯大学）；University of Alabama（阿拉巴马大学）；Software Engineering Institute, Carnegie Mellon University（卡内基梅隆大学软件工程研究所）
We propose a robust method to identify anomalous jets by vetoing QCD-jets. The robustness of this method ensures that the distribution of the proposed discriminating variable (which allows us to veto QCD-jets) remains unaffected by the phase space of QCD-jets, even if they were different from the region on which the model was trained. This suggests that our method can be used to look for anomalous jets in high m/p T bins by simply training on jets from low m/p T bins, where sufficient background-enriched data is available. The robustness follows from combining an autoencoder with a novel way o…
Learning Broken Symmetries with Approximate Invariance
Taft Charter High School STEAM Magnet（塔夫特特许高中STEAM磁校）；University of California, Irvine（加州大学欧文分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Universität Hamburg（汉堡大学）
