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Paper Citation Record · LEDGER

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks

As of 11 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.04723.

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pith.paper-citation-record.v1
2607.04723 v2

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measured 29 of 29 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-11T14:34:16.155352Z

measured 29 of 29 standing notices

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29 of 29 outbound references displayed

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Outbound references

Observation 4a3c2108-88e4-4470-8c94-65268feb8eff · outbound

This paper cites The atlas experiment at the cern large hadron collider.Journal of Instrumentation, 3(08):S08003, aug 2008.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks The atlas experiment at the cern large hadron collider.Journal of Instrumentation, 3(08):S08003, aug 2008

Reference 1

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Observation f87aba5b-912d-47e0-8a5a-64e368b46853 · outbound

This paper cites The cms experiment at the cern lhc.Journal of Instrumentation, 3(08):S08004, aug 2008.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks The cms experiment at the cern lhc.Journal of Instrumentation, 3(08):S08004, aug 2008

Reference 2

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Observation f29e8a89-aa6f-44c1-bc5a-335648fed1b3 · outbound

This paper cites The large hadron collider.Annual review of nuclear and particle science, 61(1):435– 466, 2011.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks The large hadron collider.Annual review of nuclear and particle science, 61(1):435– 466, 2011

Reference 3

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Observation aa62a55f-fcfb-4809-9dae-61bb09ed09c5 · outbound

This paper cites High-luminosity large hadron collider (hl-lhc): Technical design report.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks High-luminosity large hadron collider (hl-lhc): Technical design report

Reference 4

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Observation 51dc78db-1267-4ca0-b95d-a4a29862e6c1 · outbound

This paper cites Technical report, CERN, Geneva, 2017.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Technical report, CERN, Geneva, 2017

Reference 5

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Observation 1188320f-53ed-48f0-824b-0fdf6e3367a6 · outbound

This paper cites Technical report, CERN, Geneva, 2020.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Technical report, CERN, Geneva, 2020

Reference 6

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Observation cb362c86-81bd-467c-a066-07d7bb78a943 · outbound

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Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Unresolved cited work

Reference 7

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Observation 8c87e9c0-b02a-4260-9b23-eaa9dede5828 · outbound

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Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Unresolved cited work

Reference 8

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Observation 54851387-494e-48b8-a12d-10a122c7db1c · outbound

This paper cites Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Reference 9

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Observation f2f89f31-3937-40f6-a130-075ed4e88300 · outbound

This paper cites Use of the hough transformation to detect lines and curves in pictures.Communications of the ACM, 15(1):11–15, 1972.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Use of the hough transformation to detect lines and curves in pictures.Communications of the ACM, 15(1):11–15, 1972

Reference 10

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Observation 1915eafa-46d3-40bc-9b10-6bc75f968291 · outbound

This paper cites Alfonsi, F.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Alfonsi, F

Reference 11

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Observation bd3d9511-4325-49bc-99da-33686abb7ba1 · outbound

This paper cites Accelerating the hough transform with cuda on graphics processing units.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Accelerating the hough transform with cuda on graphics processing units

Reference 12

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Observation 6430fb08-433d-4e74-91fb-cbee6b4fbdba · outbound

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Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Unresolved cited work

Reference 13

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Observation ec2bf6b9-124e-4de9-b53e-60b880c69166 · outbound

This paper cites A brief introduction to pythia 8.1.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks A brief introduction to pythia 8.1

Reference 14

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Observation c1235dc6-b31a-4115-8d58-2aea50ec05bb · outbound

This paper cites Pythia 6.4 physics and manual.Journal of High Energy Physics, 2006(05):026, may 2006.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Pythia 6.4 physics and manual.Journal of High Energy Physics, 2006(05):026, may 2006

Reference 15

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Observation 71982297-a9f5-4edf-be4e-19806729c79f · outbound

This paper cites Agostinelli et al.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Agostinelli et al

Reference 16

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Observation 4eedffd7-9fd8-46d8-88e2-fc46fadda262 · outbound

This paper cites The open data detector tracking system.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks The open data detector tracking system

Reference 17

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Observation bff3d312-a500-4226-8af9-1e80bbac3ab2 · outbound

This paper cites Amrouche.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Amrouche

Reference 18

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Observation 620e4c8a-5b26-49b6-aebc-4986906dc22e · outbound

This paper cites The trackml high-energy physics tracking challenge on kaggle.EPJ Web Conf., 214:06037, 2019.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks The trackml high-energy physics tracking challenge on kaggle.EPJ Web Conf., 214:06037, 2019

Reference 19

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Observation 9ffd584b-d955-4816-af26-800f75f15b52 · outbound

This paper cites Amrouche.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Amrouche

Reference 20

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Observation 64000abd-16a8-4794-ab28-622e3a830239 · outbound

This paper cites Detector simulations with dd4hep.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Detector simulations with dd4hep

Reference 21

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Observation 9f93dc09-fb02-4c3d-b751-410e13291474 · outbound

This paper cites Colliderml: The first release of an opendatadetector high-luminosity physics benchmark dataset, 2025.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Colliderml: The first release of an opendatadetector high-luminosity physics benchmark dataset, 2025

Reference 22

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This paper cites Deep learning.Nature, 521(7553):436–444, May 2015.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Deep learning.Nature, 521(7553):436–444, May 2015

Reference 23

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Observation f5ad9b16-330e-4c19-a564-57ebbe3e56f3 · outbound

This paper cites Imagenet classification with deep con- volutional neural networks.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Imagenet classification with deep con- volutional neural networks

Reference 24

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Observation 0b084cb6-0443-44ec-acdb-5ec25a4d727d · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks TensorFlow: Large-scale machine learning on heterogeneous systems, 2015

Reference 25

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Observation bd00cbea-4f19-4998-9aa8-6cf578c1eab0 · outbound

This paper cites Keras.https://keras.io, 2015.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Keras.https://keras.io, 2015

Reference 26

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Observation 16bcd881-4a40-4c39-a74f-769629f6084e · outbound

This paper cites Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25, 2012.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25, 2012

Reference 27

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Observation da74be2d-735f-4fea-9ffd-22079ab1d56c · outbound

This paper cites Kerastuner.https://github.com/keras-team/keras-tuner, 2019.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Kerastuner.https://github.com/keras-team/keras-tuner, 2019

Reference 28

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This paper cites Springer London, London, 2011.

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks Springer London, London, 2011

Reference 29

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