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The TrackML high-energy physics tracking challenge on Kaggle
2019
EPJ Web of Conferences
Preserved Fulltext
To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists ...
The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. ...
We are very grateful to our generous sponsors without which the challenges would ...
doi:10.1051/epjconf/201921406037
fatcat:drsstpitlbblba65gcyoeyg62u
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.06995v1 fatcat:bpvmv5lx2vcupcj5pcipidk3nm
The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity ...
This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS ...
Introduction Charged particle tracking plays an essential role in High-Energy Physics (HEP), including particle identification and kinematics, vertex finding, lepton reconstruction, and flavor jet tagging ...
arXiv:2103.06995v2
fatcat:d5jsnbocijhc7izr54ugbjskxe
The TrackML challenge
2018
The TrackML challenge. NIPS 2018-32nd Annual Conference on Neural Information Processing Systems
unpublished
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We call our new challenge "TrackML", for Tracking trajectories of particles with Machine Learning. ...
Recently, several challenges in climate science, astrophysics, high energy physics, and chemistry have been organized as reported at the NIPS 2017 "challenges in machine learning" workshop 1 dedicated ...
fatcat:ecq3zytqhbgfpgfx7gjwx7xuzm
Particle Track Reconstruction with Quantum Algorithms
[article]
2020
arXiv
pre-print
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Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. ...
Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. ...
This work was partially supported by Turkish Atomic Energy Authority (TAEK) (Grant No: 2017TAEKCERN-A5.H6.F2.15). Cenk Tüysüz thanks Oral Okan and Egemen Sert from STB for their valuable discussions. ...
arXiv:2003.08126v1
fatcat:rsdkmbzwjvg7xh6lml4hpnlaty
TrackML : a tracking Machine Learning challenge
2019
Proceedings of The 39th International Conference on High Energy Physics — PoS(ICHEP2018)
unpublished
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We are very grateful to our generous sponsors without which the challenges would not ...
The 39th International Conference on High Energy Physics (ICHEP2018) 4-11 July, 2018 Seoul, Korea * Speaker. ...
However, variations in track efficiency as a function of η are observed.
Conclusion Overall the TrackML challenge was very well received by the Kaggle community with high levels of participation. ...
doi:10.22323/1.340.0159
fatcat:26svq2nkrfdevoxl3ojd4z7tty
Particle Track Reconstruction with Quantum Algorithms
2020
EPJ Web of Conferences
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Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. ...
Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. ...
The HepTrkX team proposed a Graph Neural Network implementation for particle track reconstruction that uses the kaggle TrackML challenge dataset [3, 7] . ...
doi:10.1051/epjconf/202024509013
fatcat:f6fpcitw45bh3gqczg55gkqwky
A pattern recognition algorithm for quantum annealers
[article]
2019
arXiv
pre-print
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The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to large increases in running time for current ...
At track densities comparable with current LHC conditions, our approach achieves physics performance competitive with state-of-the-art pattern recognition algorithms. ...
needed in quantum computers to solve problems in high-energy physics. ...
arXiv:1902.08324v1
fatcat:unqdp23syndxngjm4cz3rs73u4
Quantum annealing algorithms for track pattern recognition
2019
Zenodo
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We will conclude with future perspectives on using annealing-based algorithms for pattern recognition in high-energy physics experiments. ...
The pattern recognition of the trajectories of charged particles is at the core of the computing challenge for the HL-LHC, which is currently the center of a very active area of research. ...
(2026~) Track reconstruction • Find the correct set of hits belonging to the same particle ○ Hits are distributed according to known physics rule: helix curve, scattering with material… Large QUBOs ...
doi:10.5281/zenodo.3599355
fatcat:tfu3xhxue5dvphj773cqopcb2q
CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction
2020
Zenodo
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In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. ...
It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, processing events as a graph connecting track measurements, are a promising solution and can reduce the ...
This work was partially supported by Turkish Atomic Energy Authority (TAEK) (Grant No: 2017TAEKCERN-A5.H6.F2.15). ...
doi:10.5281/zenodo.4088473
fatcat:qeargfvfbva4xkf32vmymiapoe
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
[article]
2020
arXiv
pre-print
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In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. ...
It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, by processing events as a graph connecting track measurements can provide a promising solution by reducing ...
This work was partially supported by Turkish Atomic Energy Authority (TAEK) (Grant No: 2017TAEKCERN-A5.H6.F2.15). ...
arXiv:2007.06868v1
fatcat:5fy65bhkjjbdrcdmdamgdxt4iy
Performance of a geometric deep learning pipeline for HL-LHC particle tracking
2021
European Physical Journal C: Particles and Fields
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The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity ...
This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS ...
Introduction Charged particle tracking plays an essential role in High-Energy Physics (HEP), including particle identification and kinematics, vertex finding, lepton reconstruction, and flavor jet tagging ...
doi:10.1140/epjc/s10052-021-09675-8
fatcat:vy3cu4tzknc7nkufcctfclz3oe
Track Seeding and Labelling with Embedded-space Graph Neural Networks
[article]
2020
arXiv
pre-print
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Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding. ...
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. ...
Introduction High energy physics (HEP) experiments are designed to solve some of the most fundamental questions in the universe by probing the interactions of elementary particles in vast quantities of ...
arXiv:2007.00149v1
fatcat:n2amxcym4rginarfv7r4epf5cq
Charged particle tracking via edge-classifying interaction networks
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.16701v2 fatcat:6dqqr2wwxfbflhqyg575tnzhtmOther Versions
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. ...
In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider ...
variety of tasks in high energy physics [26, 27] . ...
arXiv:2103.16701v3
fatcat:oxyiqvl2trehpomftgfn2672w4
CTD2020: Graph Neural Networks for Track Finding
2020
Zenodo
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These feed into a high-accuracy N-plet classifier, a track parameter regression GNN, or can be used as an end-to-end track classifier by clustering in an embedded space. ...
To address the unprecedented scale of HL-LHC data, the Exa.TrkX (previously HEP.TrkX) project has been investigating a variety of machine learning approaches to particle track reconstruction. ...
Introduction High energy physics (HEP) experiments are designed to solve some of the most fundamental questions in the universe by probing the interactions of elementary particles in vast quantities of ...
doi:10.5281/zenodo.4088460
fatcat:5qzdpyuvkfau7chjcxza3rpkgm
Acts: A common tracking software
[article]
2019
arXiv
pre-print
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a much more challenging tracking environment. ...
The reconstruction of charged particle trajectories is one of the most complex and CPU consuming parts of event processing in high energy experiments. ...
Introduction The record-breaking data taking of the LHC in the second run (Run-2) between 2015 and 2018 provides chance for extraordinary exploration of the high-energy frontier. ...
arXiv:1910.03128v2
fatcat:kibqeggb35bibj675jwfmtuegu
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