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Particle Track Reconstruction with Quantum Algorithms
/ Tüysüz, Cenk (Middle East Tech. U., Ankara ; Unlisted, TR) ; Carminati, Federico (CERN) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Dobos, Daniel (Unlisted, CH ; Lancaster U. (main)) ; Fracas, Fabio (CERN) ; Novotny, Kristiane (Unlisted, CH) ; Potamianos, Karolos (Unlisted, CH ; DESY) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech)
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. [...]
arXiv:2003.08126.-
2020 - 7 p.
- Published in : EPJ Web Conf. 245 (2020) 09013
Fulltext: 2003.08126 - PDF; fulltext1785920 - PDF; Fulltext from publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.09013
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2.
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Performance of Particle Tracking Using a Quantum Graph Neural Network
/ Tüysüz, Cenk (Middle East Tech. U., Ankara) ; Novotny, Kristiane (Unlisted, CH) ; Rieger, Carla (Zurich, ETH) ; Carminati, Federico (CERN) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Dobos, Daniel (Unlisted, CH ; Lancaster U.) ; Fracas, Fabio (CERN ; Padua U.) ; Potamianos, Karolos (Unlisted, CH ; Oxford U.) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech)
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. [...]
arXiv:2012.01379.
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6 p.
Fulltext
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3.
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Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
/ Tüysüz, Cenk (Middle East Tech. U., Ankara) ; Rieger, Carla (ETH, Zurich (main)) ; Novotny, Kristiane (Lancaster U.) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Dobos, Daniel (Lancaster U.) ; Potamianos, Karolos (Oxford U.) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech) ; Forster, Richard (Lancaster U.)
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. [...]
arXiv:2109.12636.-
2021-12-01 - 20 p.
- Published in : Quant. Machine Intell. 3 (2021) 29
Fulltext: Tüysüz2021_Article_HybridQuantumClassicalGraphNeu (1) - PDF; document - PDF; 2109.12636 - PDF; Fulltext from publisher: PDF;
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4.
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A Quantum Graph Neural Network Approach to Particle Track Reconstruction
/ Tüysüz, Cenk (Middle East Tech. U., Ankara) ; Carminati, Federico (CERN) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Dobos, Daniel (Lancaster U.) ; Fracas, Fabio (CERN ; Padua U.) ; Novotny, Kristiane ; Potamianos, Karolos (DESY) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech)
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. [...]
arXiv:2007.06868.
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6 p.
Fulltext
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5.
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Quantum Track Reconstruction Algorithms for non-HEP applications
/ Novotny, Kristiane Sylvia (gluoNNet) ; Tüysüz, Cenk (Middle East Tech. U., Ankara) ; Rieger, Carla (Zurich, ETH) ; Dobos, Daniel (gluoNNet ; Lancaster U.) ; Potamianos, Karolos Jozef (gluoNNet ; Oxford U.) ; Vallecorsa, Sofia (CERN) ; Carminati, Federico (CERN) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Vlimant, Jean-Roch (Caltech) ; Fracas, Fabio (Padua U.)
The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. [...]
SISSA, 2021 - 6 p.
- Published in : PoS ICHEP2020 (2021) 983
Fulltext: PDF;
In : 40th International Conference on High Energy Physics (ICHEP), Prague, Czech Republic, 28 Jul - 6 Aug 2020, pp.983
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6.
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The TrackML high-energy physics tracking challenge on Kaggle
/ Kiehn, Moritz (Geneva U.) ; Amrouche, Sabrina (Geneva U.) ; Calafiura, Paolo (LBL, Berkeley) ; Estrade, Victor (LRI, Paris 11) ; Farrell, Steven (LBL, Berkeley) ; Germain, Cécile (LRI, Paris 11) ; Gligorov, Vava (Paris U., VI-VII) ; Golling, Tobias (Geneva U.) ; Gray, Heather (LBL, Berkeley) ; Guyon, Isabelle (INRIA, Saclay ; Unlisted, US) et al.
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. 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 building on the experience of the successful Higgs Machine Learning challenge in 2014. [...]
2019 - 7 p.
- Published in : EPJ Web Conf. 214 (2019) 06037
Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.06037
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TrackML : The High Energy Physics Tracking Challenge
/ Rousseau, David (speaker) (LAL-Orsay, FR)
At HL-LHC, the seven-fold increase of multiplicity wrt 2018 conditions poses a severe challenge to ATLAS and CMS tracking experiments. Both experiment are revamping their tracking detector, and are optimizing their software [...]
2018 - 3207.
EP-IT Data science seminars
External link: Event details
In : TrackML : The High Energy Physics Tracking Challenge
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9.
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The Acts project: track reconstruction software for HL-LHC and beyond
/ Gessinger, Paul (CERN ; Mainz U.) ; Grasland, Hadrien (CENBG, Gradignan) ; Gray, Heather (LBL, Berkeley ; UC, Berkeley) ; Kiehn, Moritz (U. Geneva (main)) ; Klimpel, Fabian (CERN ; Tech. U., Munich (main)) ; Langenberg, Robert (CERN) ; Salzburger, Andreas (CERN) ; Schlag, Bastian (CERN ; Mainz U.) ; Zhang, Jin (ICTS, Beijing) ; Ai, Xiaocong (UC, Berkeley)
The reconstruction of trajectories of the charged particles in the tracking detectors of high energy physics (HEP) experiments is one of the most difficult and complex tasks of event reconstruction at particle colliders. As pattern recognition algorithms exhibit combinatorial scaling to high track multiplicities, they become the largest contributor to the CPU consumption within event reconstruction, particularly at current and future hadron colliders such as the LHC, HL-LHC and FCC-hh. [...]
2020 - 7 p.
- Published in : EPJ Web Conf. 245 (2020) 10003
Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.10003
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10.
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The Tracking Machine Learning challenge
/ Rousseau, David (speaker) (LAL-Orsay, FR)
The HL-LHC will see ATLAS and CMS see proton bunch collisions reaching track multiplicity up to 10.000 charged tracks per event. Algorithms need to be developed to harness the increased combinatorial complexity. [...]
2019 - 1970.
LPCC Workshops; 3rd IML Machine Learning Workshop
External links: Talk details; Event details
In : 3rd IML Machine Learning Workshop
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