4-11 July 2018
Asia/Seoul timezone

TrackML : a tracking Machine Learning challenge

5 Jul 2018, 14:40
201 (COEX, Seoul)


COEX, Seoul

Parallel Computing and Data Handling Computing and Data Handling


Tobias Golling (Universite de Geneve (CH))


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] (http://acts.web.cern.ch/ACTS/latest/doc/index.html), 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 10’000 tracks (100’000 hits) per event.

The task to be performed by participants in the challenge is the “pattern recognition”: associate the hits to tracks corresponding to the original charged particles. The participants are given 100’000 events (including truth information) to train their algorithm, while the evaluation by Kaggle is run on 100 other events. The score used to rank the candidates is the fraction of hits correctly assigned, with a weighting mechanism to favor higher momentum tracks and hits in the innermost and outermost detector layers. In this challenge, there is no CPU constraint, however a second phase of the challenge to be run during the summer will have strong computational constraints.
The emphasis of the challenge is to explore innovative Machine Learning approaches, rather than hyper-optimising known combinatorial approaches. In preliminary discussions with the ML community, Convolutional Neural Network, LSTM, Deep Neural Nets, Monte Carlo Tree Search, geometric Deep Learning have been mentioned. A very simplified 2D version of the challenge (see reference) was successfully run as a two day hackathon in March 2017.

In this talk, the first lessons from the challenge (which will just have been completed) will be discussed. What algorithms have emerged and are the most promising ? How robust is the score, compared to deeper performance evaluations ?

Primary authors

Paolo Calafiura (University of California Berkeley (US)) Dr Steven Andrew Farrell (Lawrence Berkeley National Lab (US)) Heather Gray (LBNL) Dr Jean-Roch Vlimant (California Institute of Technology (US)) Vincenzo Innocente (CERN) Andreas Salzburger (CERN) Sabrina Amrouche (RSA - Universite de Geneve (CH)) Tobias Golling (Universite de Geneve (CH)) Moritz Kiehn (Universite de Geneve (CH)) Cecile Germain (Universite Paris Sud) Isabelle Guyon David Rousseau (LAL-Orsay, FR) Yetkin Yilmaz (Laboratoire Leprince-Ringuet, France) Vladimir Gligorov (Centre National de la Recherche Scientifique (FR)) Mikhail Hushchyn (Yandex School of Data Analysis (RU)) Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))


Prof. Claudia Plant (Universität Wien)

Presentation Materials