Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC Article Swipe

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S. Akar , Mohamed Elashri , R. B. Garg , Elliott Kauffman , Michael A. Peters , Henry Schreiner , M. D. Sokoloff , William Tepe , L. Tompkins ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2309.12417

We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived heuristically from the ensemble of charged track parameters and predicted "target histogram" proxies, from which the actual PV positions are extracted. We have recently demonstrated that using a UNet architecture performs indistinguishably from a "flat" convolutional neural network model. We have developed an "end-to-end" tracks-to-hist DNN that predicts target histograms directly from track parameters using simulated LHCb data that provides better performance (a lower false positive rate for the same high efficiency) than the best KDE-to-hists model studied. This DNN also provides better efficiency than the default heuristic algorithm for the same low false positive rate. "Quantization" of this model, using FP16 rather than FP32 arithmetic, degrades its performance minimally. Reducing the number of UNet channels degrades performance more substantially. We have demonstrated that the KDE-to-hists algorithm developed for LHCb data can be adapted to ATLAS and ACTS data using two variations of the UNet architecture. Within ATLAS/ACTS, these algorithms have been validated against the standard vertex finder algorithm. Both variations produce PV-finding efficiencies similar to that of the standard algorithm and vertex-vertex separation resolutions that are significantly better.

Related Topics

Large Hadron Collider

Vertex (Graph Theory)

Convolutional Neural Network

Computer Science

Algorithm

Quantization (Signal Processing)

Artificial Intelligence

Theoretical Computer Science

Concepts

Large Hadron Collider Vertex (graph theory) Convolutional neural network Computer science Estimator Algorithm Histogram Artificial neural network Quantization (signal processing) Artificial intelligence Pattern recognition (psychology) Particle physics Mathematics Physics Statistics Theoretical computer science Graph Image (mathematics)

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2309.12417
PDF: https://arxiv.org/pdf/2309.12417
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2309.12417

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Title: Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-09-21

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Authors: S. Akar, Mohamed Elashri, R. B. Garg, Elliott Kauffman, Michael A. Peters, Henry Schreiner, M. D. Sokoloff, William Tepe, L. Tompkins

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Landing page: https://arxiv.org/abs/2309.12417

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Concepts: Large Hadron Collider, Vertex (graph theory), Convolutional neural network, Computer science, Estimator, Algorithm, Histogram, Artificial neural network, Quantization (signal processing), Artificial intelligence, Pattern recognition (psychology), Particle physics, Mathematics, Physics, Statistics, Theoretical computer science, Graph, Image (mathematics)

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Cited by: 1

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abstract_inverted_index.LHC.	22
abstract_inverted_index.LHCb	32, 102, 175
abstract_inverted_index.This	124
abstract_inverted_index.UNet	74, 160, 190
abstract_inverted_index.also	126
abstract_inverted_index.been	197
abstract_inverted_index.best	120
abstract_inverted_index.data	33, 103, 176, 184
abstract_inverted_index.deep	6
abstract_inverted_index.from	47, 59, 78, 97
abstract_inverted_index.have	68, 86, 167, 196
abstract_inverted_index.high	116
abstract_inverted_index.more	164
abstract_inverted_index.rate	112
abstract_inverted_index.same	115, 137
abstract_inverted_index.than	118, 130, 149
abstract_inverted_index.that	38, 71, 92, 104, 169, 212, 221
abstract_inverted_index.this	144
abstract_inverted_index.with	40
abstract_inverted_index.work	24
abstract_inverted_index.(PVs)	16
abstract_inverted_index.ATLAS	181
abstract_inverted_index.false	110, 139
abstract_inverted_index.lower	109
abstract_inverted_index.model	122
abstract_inverted_index.rate.	141
abstract_inverted_index.these	194
abstract_inverted_index.track	52, 98
abstract_inverted_index.using	34, 72, 100, 146, 185
abstract_inverted_index.which	60
abstract_inverted_index."flat"	80
abstract_inverted_index.(DNNs)	9
abstract_inverted_index.(KDEs)	44
abstract_inverted_index.Within	192
abstract_inverted_index.actual	62
abstract_inverted_index.better	106, 128
abstract_inverted_index.finder	203
abstract_inverted_index.hybrid	36
abstract_inverted_index.kernel	41
abstract_inverted_index.locate	13
abstract_inverted_index.model,	145
abstract_inverted_index.model.	84
abstract_inverted_index.neural	7, 82
abstract_inverted_index.number	158
abstract_inverted_index.rather	148
abstract_inverted_index.target	94
abstract_inverted_index.vertex	202
abstract_inverted_index."target	56
abstract_inverted_index.Earlier	23
abstract_inverted_index.adapted	179
abstract_inverted_index.against	199
abstract_inverted_index.better.	224
abstract_inverted_index.charged	51
abstract_inverted_index.default	132
abstract_inverted_index.density	42
abstract_inverted_index.derived	45
abstract_inverted_index.finding	27
abstract_inverted_index.focused	25
abstract_inverted_index.network	83
abstract_inverted_index.primary	14, 28
abstract_inverted_index.produce	207
abstract_inverted_index.similar	210
abstract_inverted_index.started	39
abstract_inverted_index.Reducing	156
abstract_inverted_index.approach	37
abstract_inverted_index.channels	161
abstract_inverted_index.degrades	152, 162
abstract_inverted_index.directly	96
abstract_inverted_index.ensemble	49
abstract_inverted_index.identify	11
abstract_inverted_index.networks	8
abstract_inverted_index.performs	76
abstract_inverted_index.positive	111, 140
abstract_inverted_index.predicts	93
abstract_inverted_index.provides	105, 127
abstract_inverted_index.proxies,	58
abstract_inverted_index.recently	69
abstract_inverted_index.standard	201, 215
abstract_inverted_index.studied.	123
abstract_inverted_index.studying	2
abstract_inverted_index.vertices	15, 29
abstract_inverted_index.algorithm	134, 172, 216
abstract_inverted_index.developed	87, 173
abstract_inverted_index.heuristic	133
abstract_inverted_index.positions	64
abstract_inverted_index.predicted	55
abstract_inverted_index.simulated	31, 101
abstract_inverted_index.validated	198
abstract_inverted_index.PV-finding	208
abstract_inverted_index.algorithm.	204
abstract_inverted_index.algorithms	195
abstract_inverted_index.collisions	19
abstract_inverted_index.efficiency	129
abstract_inverted_index.estimators	43
abstract_inverted_index.extracted.	66
abstract_inverted_index.histogram"	57
abstract_inverted_index.histograms	95
abstract_inverted_index.minimally.	155
abstract_inverted_index.parameters	53, 99
abstract_inverted_index.separation	219
abstract_inverted_index.variations	187, 206
abstract_inverted_index.ATLAS/ACTS,	193
abstract_inverted_index.arithmetic,	151
abstract_inverted_index.efficiency)	117
abstract_inverted_index.performance	107, 154, 163
abstract_inverted_index.resolutions	220
abstract_inverted_index."end-to-end"	89
abstract_inverted_index.KDE-to-hists	121, 171
abstract_inverted_index.architecture	75
abstract_inverted_index.demonstrated	70, 168
abstract_inverted_index.efficiencies	209
abstract_inverted_index.architecture.	191
abstract_inverted_index.convolutional	81
abstract_inverted_index.heuristically	46
abstract_inverted_index.proton-proton	18
abstract_inverted_index.significantly	223
abstract_inverted_index.vertex-vertex	218
abstract_inverted_index."Quantization"	142
abstract_inverted_index.substantially.	165
abstract_inverted_index.tracks-to-hist	90
abstract_inverted_index.indistinguishably	77
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	9
sustainable_development_goals[0].id	https://metadata.un.org/sdg/9
sustainable_development_goals[0].score	0.44999998807907104
sustainable_development_goals[0].display_name	Industry, innovation and infrastructure
citation_normalized_percentile