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Author(s)
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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) ; Germaint, Cécile (LRI, Paris 11) ; Guyon, Isabelle (LRI, Paris 11) ; Moyse, Ed (Massachusetts U., Boston) ; Rousseau, David (Orsay, LAL) ; Yilmaz, Yetkin (Orsay, LAL) ; Gligorov, Vladimir Vava (Paris U., VI-VII) ; Hushchyn, Mikhail (Yandex Sch. Data Anal., Moscow) ; Ustyuzhanin, Andrey (Yandex Sch. Data Anal., Moscow) |
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Abstract
| 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. In terms of software, the increased combinatorial complexity will have to harnessed without any increase in budget. To engage the Computer Science community to contribute new ideas, we organized a Tracking Machine Learning challenge (TrackML) running on the Kaggle platform from March to June 2018, building on the experience of the successful Higgs Machine Learning challenge in 2014. The data were generated using [ACTS], an open source accurate tracking simulator, featuring a typical all silicon LHC tracking detector, with 10 layers of cylinders and disks. Simulated physics events (Pythia ttbar) overlaid with 200 additional collisions yield typically 10000 tracks (100000 hits) per event. The first lessons from the Accuracy phase of the challenge will be discussed. |