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Graph Neural Network-Based Track Finding in the LHCb Vertex Detector
/ Correia, Anthony (LPNHE, Paris) ; Giasemis, Fotis I. (LPNHE, Paris ; LIP6, Paris) ; Garroum, Nabil (LPNHE, Paris) ; Gligorov, Vladimir Vava (LPNHE, Paris ; CERN) ; Granado, Bertrand (LIP6, Paris)
The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has always been a critical part of offline data processing pipelines. [...]
arXiv:2407.12119.-
2024-12-17 - 17 p.
- Published in : JINST 19 (2024) P12022
Fulltext: 2407.12119 - PDF; document - PDF;
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Looking Forward: A High-Throughput Track Following Algorithm for Parallel Architectures
/ Bailly-Reyre, Aurelien (Paris U., VI-VII) ; Bian, Lingzhu (Wuhan U.) ; Billoir, Pierre (Paris U., VI-VII) ; Campora Perez, Daniel Hugo (Maastricht U.) ; Gligorov, Vladimir Vava (LPNHE, Paris) ; Pisani, Flavio (CERN) ; Quagliani, Renato (LPNHE, Paris ; CERN ; Ecole Polytechnique, Lausanne) ; Scarabotto, Alessandro (Paris U., VI-VII ; Tech. U., Dortmund (main)) ; vom Bruch, Dorothea (Marseille, CPPM)
Real-time data processing is a central aspect of particle physics experiments with high requirements on computing resources. The LHCb experiment must cope with the 30 million proton-proton bunches collision per second rate of the Large Hadron Collider (LHC), producing $10^9$ particles/s. [...]
arXiv:2402.14670.-
2024 - 14 p.
- Published in : IEEE Access 12 (2024) 114198-114211
Fulltext: Publication - PDF; 2402.14670 - PDF;
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Evolution of the energy efficiency of LHCb’s real-time processing
/ Aaij, Roel (NIKHEF, Amsterdam) ; Cámpora Pérez, Daniel Hugo (Maastricht U.) ; Colombo, Tommaso (CERN) ; Fitzpatrick, Conor (Manchester U.) ; Gligorov, Vladimir Vava (Paris U., VI-VII) ; Hennequin, Arthur (CERN ; LIP6, Paris) ; Neufeld, Niko (CERN) ; Nolte, Niklas (MIT) ; Schwemmer, Rainer (CERN) ; Vom Bruch, Dorothea (Marseille, CPPM)
The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4~TB/s in real time. Because LHCb's physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. [...]
arXiv:2106.07701.-
2021 - 9 p.
- Published in : EPJ Web Conf. 251 (2021) 04009
Fulltext: 2106.07701 - PDF; document - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.04009
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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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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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6.
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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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7.
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The strange side of LHCb
/ Borsato, Martino (Santiago de Compostela U., IGFAE) ; Gligorov, Vladimir Vava (Paris U., VI-VII) ; Guadagnoli, Diego (Annecy, LAPTH) ; Martinez Santos, Diego (Santiago de Compostela U., IGFAE) ; Sumensari, Olcyr (Padua U. ; INFN, Padua)
We provide general effective-theory arguments relating present-day discrepancies in semi-leptonic $B$-meson decays to signals in kaon physics, in particular lepton-flavour violating ones of the kind $K \to (\pi) e^\pm \mu^\mp$. We show that $K$-decay branching ratios of around $10^{-12} - 10^{-13}$ are possible, for effective-theory cutoffs around $5-15$ TeV compatible with discrepancies in $B\to K^{(\ast)} \mu\mu$ decays. [...]
arXiv:1808.02006; LAPTH-030/18.-
2019-03-13 - 7 p.
- Published in : Phys. Rev. D 99 (2019) 055017
Article from SCOAP3: PDF;
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Selection and processing of calibration samples to measure the particle identification performance of the LHCb experiment in Run 2
/ Aaij, Roel (NIKHEF, Amsterdam) ; Anderlini, L. (INFN, Florence) ; Benson, S. (NIKHEF, Amsterdam) ; Cattaneo, Marco (CERN) ; Charpentier, Philippe (CERN) ; Clemencic, Marco (CERN) ; Falabella, Antonio (INFN, Bologna) ; Ferrari, Fabio (INFN, Bologna) ; Fontana, M. (CERN) ; Gligorov, Vladimir Vava (Paris U., VI-VII) et al.
Since 2015, with the restart of the LHC for its second run of data taking, the LHCb experiment has been empowered with a dedicated computing model to select and analyse calibration samples to measure the performance of the particle identification (PID) detectors and algorithms. The novel technique was developed within the framework of the innovative trigger model of the LHCb experiment, which relies on online event reconstruction for most of the datasets, reserving offline reconstruction to special physics cases. [...]
arXiv:1803.00824; LHCb-DP-2018-001; LHCB-DP-2018-001.-
2019-02-28 - 18 p.
- Published in : EPJ Tech. Instrum. 6 (2019) 1
Fulltext from Publisher: PDF;
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10.
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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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