Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC Article Swipe

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Breno Orzari , T. R. Fernandez Perez Tomei , M. Pierini , Mary Touranakou , J. Duarte , R. Kansal , Jean-Roch Vlimant , Dimitrios Gunopulos ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2109.15197

We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of hadronic jets due to its sparsity and the possibility of evaluating the network's ability to accurately describe the particles and jets properties. A variational autoencoder composed of convolutional layers in the encoder and decoder is used as the generator. The loss function consists of a reconstruction error term and the Kullback-Leibler divergence between the output of the encoder and the latent vector variables. The permutation-invariant loss on the particles' properties is combined with two mean-squared error terms that measure the difference between input and output jets mass and transverse momentum, which improves the network's generation capability as it imposes physics constraints, allowing the model to learn the kinematics of the jets.

Related Topics

Autoencoder

Hadron

Physics

Large Hadron Collider

Algorithm

Particle Physics

Artificial Intelligence

Concepts

Autoencoder Hadron Physics Large Hadron Collider Invariant (physics) Divergence (linguistics) Generator (circuit theory) Measure (data warehouse) Resampling Artificial neural network Algorithm Permutation (music) Invariant mass Particle physics Statistical physics Artificial intelligence Computer science Quantum mechanics Linguistics Database Power (physics) Philosophy Acoustics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2109.15197
PDF: https://arxiv.org/pdf/2109.15197
OA Status: green
Cited By: 3
Related Works: 10
OpenAlex ID: https://openalex.org/W4286951228

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DOI: https://doi.org/10.48550/arxiv.2109.15197

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Title: Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-09-30

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Authors: Breno Orzari, T. R. Fernandez Perez Tomei, M. Pierini, Mary Touranakou, J. Duarte, R. Kansal, Jean-Roch Vlimant, Dimitrios Gunopulos

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Landing page: https://arxiv.org/abs/2109.15197

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Concepts: Autoencoder, Hadron, Physics, Large Hadron Collider, Invariant (physics), Divergence (linguistics), Generator (circuit theory), Measure (data warehouse), Resampling, Artificial neural network, Algorithm, Permutation (music), Invariant mass, Particle physics, Statistical physics, Artificial intelligence, Computer science, Quantum mechanics, Linguistics, Database, Power (physics), Philosophy, Acoustics

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Cited by: 3

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abstract_inverted_index.allowing	136
abstract_inverted_index.combined	106
abstract_inverted_index.composed	60
abstract_inverted_index.consists	29, 77
abstract_inverted_index.describe	51
abstract_inverted_index.function	76
abstract_inverted_index.hadronic	35
abstract_inverted_index.improves	126
abstract_inverted_index.particle	13, 32
abstract_inverted_index.sparsity	40
abstract_inverted_index.function.	21
abstract_inverted_index.momentum,	124
abstract_inverted_index.network's	47, 128
abstract_inverted_index.particles	53
abstract_inverted_index.accurately	50
abstract_inverted_index.capability	130
abstract_inverted_index.difference	115
abstract_inverted_index.divergence	86
abstract_inverted_index.evaluating	45
abstract_inverted_index.generation	8, 129
abstract_inverted_index.generative	3
abstract_inverted_index.generator.	73
abstract_inverted_index.kinematics	142
abstract_inverted_index.particles'	103
abstract_inverted_index.properties	104
abstract_inverted_index.transverse	123
abstract_inverted_index.variables.	97
abstract_inverted_index.autoencoder	59
abstract_inverted_index.possibility	43
abstract_inverted_index.properties.	56
abstract_inverted_index.variational	58
abstract_inverted_index.constituents	33
abstract_inverted_index.constraints,	135
abstract_inverted_index.mean-squared	109
abstract_inverted_index.convolutional	62
abstract_inverted_index.reconstruction	80
abstract_inverted_index.Kullback-Leibler	85
abstract_inverted_index.physics-informed	19
abstract_inverted_index.permutation-invariant	17, 99
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	8
sustainable_development_goals[0].id	https://metadata.un.org/sdg/7
sustainable_development_goals[0].score	0.47999998927116394
sustainable_development_goals[0].display_name	Affordable and clean energy
citation_normalized_percentile