1.
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TrackML: A High Energy Physics Particle Tracking Challenge
/ Calafiura, Polo (LBNL, Berkeley) ; Farrell, Steven (LBNL, Berkeley) ; Gray, Heather (LBNL, Berkeley) ; Vlimant, Jean-Roch (Caltech) ; Innocente, Vincenzo (CERN) ; Salzburger, Andreas (CERN) ; Amrouche, Sabrina (Geneva U.) ; Golling, Tobias (Geneva U.) ; Kiehn, Moritz (Geneva U.) ; Estrade, Victor (LRI, Paris 11) et al.
To attain its ultimate discovery goals, the luminosity of the Large Hadron Collider at CERN will increase so the amount of additional collisions will reach a level of 200 interaction per bunch crossing, a factor 7 w.r.t the current (2017) luminosity. This will be a challenge for the ATLAS and CMS experiments, in particular for track reconstruction algorithms. [...]
2018 - 1 p.
- Published in : 10.1109/eScience.2018.00088
In : 14th eScience IEEE International Conference, Amsterdam, Netherlands, 29 Oct - 1 Nov 2018, pp.344
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2.
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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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3.
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The Tracking Machine Learning challenge : Accuracy phase
/ Amrouche, Sabrina (Geneva U.) ; Basara, Laurent (LRI, Paris 11) ; Calafiura, Paolo (LBL, Berkeley) ; Estrade, Victor (LRI, Paris 11) ; Farrell, Steven (LBL, Berkeley) ; Ferreira, Diogo R. (Lisbon, IST) ; Finnie, Liam (IBM, Boblingen) ; Finnie, Nicole (CFEL, Hamburg) ; Germain, Cécile (LRI, Paris 11) ; Gligorov, Vladimir Vava (Paris U., VI-VII) et al.
This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML) [...]
arXiv:1904.06778.-
2020 - 36 p.
- Published in : 10.1007/978-3-030-29135-8_9
Fulltext: PDF;
In : 32nd Annual Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2 - 8 Dec 2018, pp.231-264
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4.
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The Tracking Machine Learning challenge : Throughput phase
/ Amrouche, Sabrina (Geneva U.) ; Basara, Laurent (LRI, Paris 11 ; INRIA, Saclay) ; Calafiura, Paolo (LBL, Berkeley ; UC, Berkeley (main) ; UC, Berkeley) ; Emeliyanov, Dmitry (Rutherford) ; Estrade, Victor (LRI, Paris 11 ; INRIA, Saclay) ; Farrell, Steven (LBL, Berkeley ; UC, Berkeley (main) ; UC, Berkeley) ; Germain, Cécile (LRI, Paris 11 ; INRIA, Saclay) ; Gligorov, Vladimir Vava (LPNHE, Paris) ; Golling, Tobias (Geneva U.) ; Gorbunov, Sergey (Goethe U., Frankfurt (main)) et al.
This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. [...]
arXiv:2105.01160.-
2023-02-13 - 19 p.
- Published in : Comput. Softw. Big Sci. 7 (2023) 1
Fulltext: 2105.01160 - PDF; Publication - PDF;
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5.
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TrackML : a tracking Machine Learning challenge
/ Golling, Tobias (Geneva U.) ; Amrouche, Sabrina (Geneva U.) ; Kiehn, Moritz (Geneva U.) ; Calafiura, Paolo (LBL, Berkeley) ; Farrell, Steven (LBL, Berkeley) ; Gray, Heather M (LBL, Berkeley) ; Estrade, Victor (U. Paris-Saclay) ; Germain, Cécile (U. Paris-Saclay) ; Gligorov, Vava (Paris U., VI-VII) ; Guyon, Isabelle (INRIA, Saclay) et al.
The High-Luminosity LHC will see pileup levels reaching 200, which will greatly increase the complexity of the tracking component of the event reconstruction. To reach out to Computer Science specialists, a Tracking Machine Learning challenge (TrackML) was set up on Kaggle in 2018 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. [...]
SISSA, 2019 - 4 p.
- Published in : PoS ICHEP2018 (2019) 159
Fulltext: PDF;
In : XXXIX International Conference on High Energy Physics, Seoul, Korea, 4 - 11 Jul 2018, pp.159
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6.
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Track reconstruction at LHC as a collaborative data challenge use case with RAMP
/ Amrouche, Sabrina (Geneva U.) ; Braun, Nils (KIT, Karlsruhe) ; Calafiura, Paolo (LBL, Berkeley) ; Farrell, Steven (LBL, Berkeley) ; Gemmler, Jochen (KIT, Karlsruhe) ; Germain, Cécile (Orsay, LAL ; LRI, Paris 11) ; Gligorov, Vladimir Vava (Paris U., VI-VII) ; Golling, Tobias (Geneva U.) ; Gray, Heather (LBL, Berkeley) ; Guyon, Isabelle (LRI, Paris 11) et al.
Charged particle track reconstruction is a major component of data-processing in high-energy physics experiments such as those at the Large Hadron Collider (LHC), and is foreseen to become more and more challenging with higher collision rates. A simplified two-dimensional version of the track reconstruction problem is set up on a collaborative platform, RAMP, in order for the developers to prototype and test new ideas. [...]
2017 - 12 p.
- Published in : EPJ Web Conf. 150 (2017) 00015
In : Connecting The Dots / Intelligent Trackers 2017, Orsay, France, 6 - 9 Mar 2017, pp.00015
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7.
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Anomaly Detection With Conditional Variational Autoencoders
/ Pol, Adrian Alan (CERN ; LRI, Paris 11) ; Berger, Victor (LRI, Paris 11) ; Cerminara, Gianluca (CERN) ; Germain, Cecile (LRI, Paris 11) ; Pierini, Maurizio (CERN)
Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. [...]
arXiv:2010.05531.
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8 p.
Fulltext
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8.
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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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9.
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Tracking at LHC as a collaborative data challenge use case with RAMP
/ Amrouche, Sabrina (University of Geneva, Switzerland) ; Braun, Nils (Karlsruhe Institute of Technology, Germany) ; Calafiura, Paolo (Lawrence Berkeley National Laboratory, CA, USA) ; Farrell, Steven (Lawrence Berkeley National Laboratory, CA, USA) ; Gammler, Jochen (Karlsruhe Institute of Technology, Germany) ; Germain, Cécile (LAL and LRI, Orsay, France) ; Gligorov, Vladimir Vava (LPNHE, Paris, France) ; Golling, Tobias (University of Geneva, Switzerland) ; Grasland, Hadrien (LAL, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France) ; Gray, Heather (Lawrence Berkeley National Laboratory, CA, USA) et al.
Charged particle tracking has been a major component of data-processing in high-energy physics experiments such as the Large Hadron Collider (LHC), and is fore- seen to become more and more challenging. There are many ways to perform the tracking task; a collaborative platform, RAMP, has been set up so that developers can create algo- rithms to solve a simplified 2D tracking problems. [...]
AIDA-2020-CONF-2017-002.-
Geneva : CERN, 2017
- Published in :
Fulltext: PDF;
In : Connecting The Dots / Intelligent Trackers 2017, Orsay, France, 6 - 9 Mar 2017
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10.
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Applying and optimizing the Exa.TrkX Pipeline on the OpenDataDetector with ACTS
/ Calafiura, Paolo (LBL, Berkeley) ; Heinrich, Lukas (Munich, Tech. U.) ; Huth, Benjamin (Regensburg U.) ; Ju, Xiangyang (LBL, Berkeley) ; Lazar, Alina (Ohio State U., Dept. Math.) ; Murnane, Daniel (LBL, Berkeley) ; Salzburger, Andreas (CERN) ; Wettig, Tilo (Regensburg U.)
Machine learning is a promising field to augment and potentially replace part of the event recon-
struction of high-energy physics experiments. This is partly due to the fact that many machine-
learning algorithms offer relatively easy portability to heterogeneous hardware and thus could
play an important role in controlling the computing budget of future experiments. [...]
2022 - 6 p.
- Published in : PoS ICHEP2022 (2022) 227
Fulltext: PDF;
In : 41st International Conference on High Energy Physics (ICHEP 2022), Bologna, Italy, 6 - 13 Jul 2022, pp.227
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