The Tracking Machine Learning Challenge: Accuracy Phase

Amrouche, Sabrina; Basara, L.; Calafiura, P.; Estrade, Victor; Farrell, S.; Ferreira, Diogo R.; Finnie, Liam; Finnie, Nicole; Germain, Cécile; Gligorov, Vladimir; Golling, T.; Gorbunov, S.; Gray, H. M.; Guyon, Isabelle; Hushchyn, M.; Innocente, V.; Kiehn, M.; Moyse, E. J. W.; Puget, Jean‐François; Reina, Yuval; Rousseau, D.; Salzburger, A.; Ustyuzhanin, A.; Vlimant, J. R.; Wind, Johan Sokrates; Xylouris, Trian; Yilmaz, Y.

doi:10.1007/978-3-030-29135-8_9

Cited by 53 publications

(78 citation statements)

References 43 publications

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“…Assuming The number of 80 is chosen such that the hits from quirk are kept as much as possible, while maintaining an affordable time consumption. Considering that the trajectories are a slightly distorted arc of helices due to experimental effects [59]. the origin is contained in the quirk plane, the plane normal vector can be determined for any two hits:…”

Section: B Quirk Signal Selectionsmentioning

confidence: 99%

Uncovering quirk signal via energy loss inside tracker

Pei

et al. 2020

Phys. Rev. D

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A quirk propagating through a detector is subject to the Lorentz force, a new confining gauge force, and the frictional force from ionization energy loss. At the LHC, it was found that the monojet search and the coplanar search were able to constrain such a quirk signal. Inspired by the coplanar search proposed by Knapen et al. [Phys. Rev. D 96, 115015 (2017)], we develop a new search that also utilizes the information of the relatively large ionization energy loss inside the tracker. Our algorithm has improved efficiency in finding quirk signals with a wide oscillation amplitude. Because of our trigger strategy, the Zð→ννÞ þ jets process overlaid by pileup events is the dominant background. We find that the ∼100 fb −1 dataset at the LHC will be able to probe the colored fermion (scalar) quirks with masses up to 2.1 (1.1) TeV and the color neutral fermion (scalar) quirks with masses up to 450 (150) GeV, respectively.

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Section: B Quirk Signal Selectionsmentioning

confidence: 99%

Uncovering quirk signal via energy loss inside tracker

Pei

et al. 2020

Phys. Rev. D

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“…The HepTrkX team proposed a Graph Neural Network implementation for particle track reconstruction that uses the kaggle TrackML challenge dataset [3,7]. The simulated dataset and the challenge was created by CERN scientists to invite machine learning experts to come up with novel methods to track reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Particle Track Reconstruction with Quantum Algorithms

et al. 2020

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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. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical “closeness” of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much “cleaner” initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.

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“…Recent novel techniques could solve the track pattern recognition task with a better performance. In the TrackML Particle Tracking Challenge [2] held at Kaggle and CodaLab, several winner algorithms [3] use a machine learning technique to improve the tracking performance. The HEP.TrkX [4] project also proposed new algorithms with learning methods based on deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Quantum annealing algorithms for track pattern recognition

et al. 2020

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The High-Luminosity Large Hadron Collider (HL-LHC) starts from 2027 to extend the physics discovery potential at the energy frontier. The HL-LHC produces experimental data with a much higher luminosity, requiring a large amount of computing resources mainly due to the complexity of a track pattern recognition algorithm. Quantum annealing might be a solution for an efficient track pattern recognition in the HL-LHC environment. We demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer. The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. The tracking efficiency and purity for the Fujitsu Digital Annealer are nearly the same as the classical simulated annealing.

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The Tracking Machine Learning Challenge: Accuracy Phase

Abstract: This paper reports the results of an experiment in high energy physics

Cited by 53 publications

References 43 publications

Uncovering quirk signal via energy loss inside tracker

Uncovering quirk signal via energy loss inside tracker

Particle Track Reconstruction with Quantum Algorithms

Quantum annealing algorithms for track pattern recognition

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