Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/eScience.2018.00088 | 2018 IEEE 14th International Conference on e-Science (e-Science) |
Keywords | Field | DocType |
CERN,ATLAS experiment,CMS experiment,track reconstruction algorithms,software,combinatorial complexity,Computer Science community,Tracking Machine Learning challenge,Kaggle platform,Higgs Machine Learning challenge,open source accurate tracking simulator,silicon LHC tracking detector,physics events,Pythia,Large Hadron Collider,throughput phase,accuracy phase,high energy physics particle tracking challenge,TrackML | Large Hadron Collider,Computer science,Computational science,Throughput,Numerical analysis,Detector,Particle,Distributed computing | Conference |
ISSN | ISBN | Citations |
2325-372X | 978-1-5386-9157-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 18 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Calafiura | 1 | 0 | 0.34 |
| Steven Farrell | 2 | 0 | 0.68 |
| Heather Gray | 3 | 0 | 0.34 |
| Jean-Roch Vlimant | 4 | 1 | 3.38 |
| Vincenzo Innocente | 5 | 0 | 0.34 |
| Andreas Salzburger | 6 | 0 | 0.68 |
| Sabrina Amrouche | 7 | 0 | 0.68 |
| Tobias Golling | 8 | 0 | 1.01 |
| Moritz Kiehn | 9 | 0 | 0.68 |
| Victor Estrade | 10 | 0 | 0.34 |
| Cécile Germaint | 11 | 0 | 0.34 |
| Isabelle Guyon | 12 | 11033 | 1544.34 |
| Ed Moyse | 13 | 0 | 0.34 |
| David Rousseau | 14 | 70 | 14.44 |
| Yetkin Yilmaz | 15 | 0 | 0.34 |
| vladimir v gligorov | 16 | 0 | 1.01 |
| Mikhail Hushchyn | 17 | 0 | 0.34 |
| a ustyuzhanin | 18 | 5 | 5.85 |