Reconstructing boosted Higgs jets from event image segmentation

Li, Jinmian; Li, Tianjun; Xu, Fangzhou

doi:10.1007/jhep04(2021)156

Cited by 15 publications

(10 citation statements)

References 62 publications

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“…[64] and then studied in refs. [65][66][67][68][69][70][71][72][73][74][75][76][77][78]. In this way, the calorimeter is regarded as a camera and the jets are represented as images in which the pixel intensities are the energy depositions of the particles within the jet.…”

Section: Jhep11(2021)138mentioning

confidence: 99%

Detecting an axion-like particle with machine learning at the LHC

Ren

Wang

et al. 2021

J. High Energ. Phys.

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Axion-like particles (ALPs) appear in various new physics models with spon- taneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we investi- gate such light ALPs through the ALP-strahlung production processes pp → W±a, Za with the sequential decay a → γγ at the 14 TeV LHC with an integrated luminosity of 3000 fb−1 (HL-LHC). Building on the concept of jet image which uses calorimeter towers as the pixels of the image and measures a jet as an image, we investigate the potential of machine learning techniques based on convolutional neural network (CNN) to identify the highly boosted ALPs which decay to a pair of highly collimated photons. With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3 GeV to 5 GeV. The obtained bounds are stronger than the existing limits on the ALP-photon coupling.

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Section: Jhep11(2021)138mentioning

confidence: 99%

Detecting an axion-like particle with machine learning at the LHC

Ren

Wang

et al. 2021

J. High Energ. Phys.

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“…Based on the theory and phenomenology of jet physics, many expert-designed high-level jet substructure observables are constructed for jet tagging [2][3][4][5][6][7]. Using different jet representations, various deep learning approaches have been investigated in recent years: Multilayer perceptrons (MLPs) can be trained on a collection of jet-level observables [8][9][10][11][12][13]; 2D convolutional neural networks (CNNs) are applied to jet images [14][15][16][17][18][19][20][21][22][23][24][25][26][27]; MLPs, 1D CNNs, and recurrent neural networks are used to process a jet as a sequence of its constituent particles [28][29][30][31][32][33][34][35][36]; Graph neural networks (GNNs) are developed for the "particle cloud", i.e., an unordered set of particles [37][38][39][40][41][42][43][44][45][46]…”

Section: Introductionmentioning

confidence: 99%

An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging

Gong,

Meng,

Zhang

et al. 2022

Preprint

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Deep learning methods have been increasingly adopted to study jets in particle physics. However, most of these methods use neural networks as black boxes and fail to incorporate Lorentz group equivariance, a fundamental spacetime symmetry for elementary particles. In this article, we introduce LorentzNet, a new symmetry-preserving deep learning model for jet tagging. 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.

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“…Recently, there has been significant interest in the use of supervised machine learning (ML) to perform the reconstruction in order to improve the physics reach of the experiments as well as reduce the computational requirements. ML-based reconstruction approaches using graph neural networks (GNNs) [14,15,16,17,18] have been proposed for various tasks in particle physics [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33], including PF reconstruction [34,35,36,37]. In particular in Ref.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Particle Flow Reconstruction at CMS

Pata¹,

Duarte²,

Mokhtar³

et al. 2022

Preprint

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We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy. We also report the computational performance of the algorithm, which scales approximately linearly in runtime and memory usage with the input size.

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Reconstructing boosted Higgs jets from event image segmentation

Cited by 15 publications

References 62 publications

Detecting an axion-like particle with machine learning at the LHC

Detecting an axion-like particle with machine learning at the LHC

An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging

Machine Learning for Particle Flow Reconstruction at CMS

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