Conclusions from TrackML the HEP Tracking Machine Learning ...€¦ · TrackML conclusion 4 See...
Transcript of Conclusions from TrackML the HEP Tracking Machine Learning ...€¦ · TrackML conclusion 4 See...
Conclusions from TrackMLthe HEP Tracking
Machine Learning Challenge
Organization
Jean-Roch Vlimant (Caltech)
Vincenzo Innocente, Andreas Salzburger (CERN)
David Rousseau, Yetkin Yilmaz (LAL-Orsay)
Paolo Calafiura, Steven Farrell, Heather Gray (LBNL)
Vladimir Gligorov (LPNHE-Paris)
Sabrina Amrouche, Tobias Golling, Moritz Kiehn (Geneva University)
Laurent Basara, Cécile Germain, Isabelle Guyon, Victor Estrade (LRI-Orsay)
Edward Moyse (University of Massachussets)
Mikhail Hushchyn, Andrey Ustyuzhanin (Yandex, HSE)
TrackML conclusion 2
Tracking crisis
● Tracking dominates reconstruction CPU time
● At best quadratic● HL-LHC (2025) :
unmanageable ● Everything tried?
→ TrackML challenge
TrackML conclusion 3
TrackML conclusion 4
See also in outreach session talk by D. Rousseau« TrackML : the roller coaster of organizing a HEP challenge on Kaggle and Codalab » https://indico.cern.ch/event/577856/contributions/3423422/
TrackML conclusion 5
See also in outreach session talk by D. Rousseau« TrackML : the roller coaster of organizing a HEP challenge on Kaggle and Codalab » https://indico.cern.ch/event/577856/contributions/3423422/
TrackML conclusion 6
First phase :Accuracy May – August 2018
TrackML conclusion 7
Leaderboard scores
TrackML conclusion 8
Optimizing score optimizes physics
TrackML conclusion 9
Participants dendrogram
TrackML conclusion 10
Phase 1 winner : Top Quarks
TrackML conclusion 11
Phase 1 #2 : outrunner
● Train DNN on hit pairs
▸ 27 inputs (x,y,z,cells,…)
▸ 4k-2k-2k-2k-1k hidden layers
● Compute full hit adjacency matrix:
▸ probability P(i,j) that 2 hits match
▸ Pick high probability comb
● True Deep Learning Solution
▸ No track following
▸ No geometric modelling
● 1 Day / event
TrackML conclusion 12
Second phase :ThroughputOct 2018 – March 2019
TrackML conclusion 13
Leaderboard evolution
TrackML conclusion 14
Leaderboard evolution
TrackML conclusion 15
Leaderboard evolution
TrackML conclusion 16
Final leaderboard
TrackML conclusion 17
#1 S. Gorbunov: « fast combinatorial »
https://indico.cern.ch/event/813759/contributions/3479705/attachments/1871795/3080436/trackML_Gorbunov-CERN2019.pdf
TrackML conclusion 18
#2 FASTrack: Graph of neighbours, cellular automata and Kalman filter
https://indico.cern.ch/event/813759/contributions/3479706/attachments/1870758/3078234/TheTrackML_workshop_talk.pdf
TrackML conclusion 19https://indico.cern.ch/event/813759/contributions/3479709/attachments/1871944/3080707/TrackML.Kunze.pdf
TrackML conclusion 20
Conclusions
● Open tracking competition organised to reach out to CS and ML communities
● Winner and runner-up HEP tracking experts…
● Retained solution will be blend from HEP expertise and new ideas
● Dataset released on CERN Open Data Portalto serve as benchmark
● Ongoing work
TrackML conclusion 21
Contacts
● Contact : [email protected]● https://sites.google.com/site/trackmlparticle● Twitter : @trackmllhc● Accuracy phase @ Kaggle:
https://www.kaggle.com/c/trackml-particle-identification▸ Chapter in the NeurIPS2018 Competition book arXiv:1904.06778
● Throughput phase @ Codalab: https://competitions.codalab.org/competitions/20112
▸ Write-up to be finalised
● TrackML challenge Grand Finale: https://indico.cern.ch/event/813759/
Conclusions from TrackMLthe HEP Tracking
Machine Learning Challenge
Organization
Jean-Roch Vlimant (Caltech)
Vincenzo Innocente, Andreas Salzburger (CERN)
David Rousseau, Yetkin Yilmaz (LAL-Orsay)
Paolo Calafiura, Steven Farrell, Heather Gray (LBNL)
Vladimir Gligorov (LPNHE-Paris)
Sabrina Amrouche, Tobias Golling, Moritz Kiehn (Geneva University)
Laurent Basara, Cécile Germain, Isabelle Guyon, Victor Estrade (LRI-Orsay)
Edward Moyse (University of Massachussets)
Mikhail Hushchyn, Andrey Ustyuzhanin (Yandex, HSE)
TrackML conclusion 23
TrackML conclusion 24
TrackML conclusion 25
Winning solution not ML→
Backup
TrackML conclusion 26
Winning solution not ML→
TrackML conclusion 27
Winning solution not ML→
TrackML conclusion 28
Winning solution not ML→
TrackML conclusion 29
Winning solution not ML→
TrackML conclusion 30
Yuval & Trian (#7)
TrackML conclusion 31
TrackML conclusion 32
TrackML conclusion 33
TrackML conclusion 34
Other contestants● #7 : Yuval & Trian
▸ « Binned randomized Hough transform » for clustering
▸ ML (LightGBM) to merge tracks
● # 9 : CPMP - « DBSCAN Forever »▸ DBSCAN on transformed space including deviation from
helix
▸ On each iteration clusters = new candidate tracks, merged
● #12 : The Finnies▸ DBSCAN variants 5 hit track seeding→
▸ LSTM estimate 10 hits→
▸ KNN for Track fitting
TrackML conclusion 35
Majority particle(truth)
Examined track(reconstructed)
Nmaj N
reco
Ngood
Puritymaj
= Ngood
/ Nmaj
Purityreco
= Ngood
/ Nreco
Scoring
TrackML conclusion 36
TrackML conclusion 37
« passing through origin »
Scattering in detector
« homogeneous magnetic field »
Energy (hence momentum) loss
TrackML conclusion 38