Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC

Ren, Jie; Wu, Lei; Yang, Jin Min

doi:10.1016/j.physletb.2020.135198

Cited by 53 publications

(26 citation statements)

References 35 publications

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“…Another important ML method, that could be explored in the context of displaced objects, is Graph Neural Network (GNN), which directly operates on the graph structure. GNNs have found extensive use in many other high-energy physics applications [93][94][95].…”

Section: Discussionmentioning

confidence: 99%

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network

Bhattacherjee

Mukherjee

Sengupta

2019

J. High Energ. Phys.

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Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machinelearning techniques have potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.

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Section: Discussionmentioning

confidence: 99%

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network

Bhattacherjee

Mukherjee

Sengupta

2019

J. High Energ. Phys.

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“…In section 2, we first introduce a graph neural network [112][113][114][115][116] with constraints, and the network is more restrictive than CNN. Graph networks are flexible enough for analyzing multiple objects appears at the LHC, and have been studied in various contexts [16,20,25,41,[117][118][119][120][121][122][123][124]. The graph network in this paper has access to only IRC safe two-point energy correlations [19,21,[96][97][98][125][126][127][128].…”

Section: Jhep07(2020)111mentioning

confidence: 99%

Neural network-based top tagger with two-point energy correlations and geometry of soft emissions

Chakraborty

Lim

Nojiri

et al. 2020

J. High Energ. Phys.

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Deep neural networks trained on jet images have been successful in classifying different kinds of jets. In this paper, we identify the crucial physics features that could reproduce the classification performance of the convolutional neural network in the top jet vs. QCD jet classification. We design a neural network that considers two types of substructural features: two-point energy correlations, and the IRC unsafe counting variables of a morphological analysis of jet images. The new set of IRC unsafe variables can be described by Minkowski functionals from integral geometry. To integrate these features into a single framework, we reintroduce two-point energy correlations in terms of a graph neural network and provide the other features to the network afterward. The network shows a comparable classification performance to the convolutional neural network. Since both networks are using IRC unsafe features at some level, the results based on simulations are often dependent on the event generator choice. We compare the classification results of Pythia 8 and Herwig 7, and a simple reweighting on the distribution of IRC unsafe features reduces the difference between the results from the two simulations.

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“…Although geometric approaches [114] exist to counter the non-Euclidean nature, the number of dimensions makes it computationally expensive. Graph neural networks [115][116][117][118] provide a possible workaround which is computationally less intensive, for feature learning in non-Euclidean domains.…”

Section: Data Representation For the Networkmentioning

confidence: 99%

Invisible Higgs search through vector boson fusion: a deep learning approach

et al. 2020

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Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the Higgs boson or for new physics. We explore the merit of deep-learning entirely from the low-level calorimeter data in the search for invisibly decaying Higgs. Such an effort supersedes decades-old faith in the remarkable event kinematics and radiation pattern as a signature to the absence of any color exchange between incoming partons in the vector boson fusion mechanism. We investigate among different neural network architectures, considering both low-level and high-level input variables as a detailed comparative analysis. To have a consistent comparison with existing techniques, we closely follow a recent experimental study of CMS search on invisible Higgs with 36 fb$$^{-1}$$ - 1 data. We find that sophisticated deep-learning techniques have the impressive capability to improve the bound on invisible branching ratio by a factor of three, utilizing the same amount of data. Without relying on any exclusive event reconstruction, this novel technique can provide the most stringent bounds on the invisible branching ratio of the SM-like Higgs boson. Such an outcome has the ability to constraint many different BSM models severely.

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Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC

Cited by 53 publications

References 35 publications

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network

Neural network-based top tagger with two-point energy correlations and geometry of soft emissions

Invisible Higgs search through vector boson fusion: a deep learning approach

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