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3rd ATLAS Machine Learning Workshop

15–17 Oct 2018
CERN
Europe/Zurich timezone

Contribution List

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  1. 1. Constraining Effective Field Theories with Machine Learning
    Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez
    17/10/2018, 09:00

    We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte-Carlo simulations, which can be used to train neural network models that estimate the likelihood ratio. These methods scale well to processes with many observables and theory parameters, do not require...
  2. 32. How to train taggers on data
    Jennifer Thompson (ITP Heidelberg)
    17/10/2018, 09:25

    The machine learning methods currently used in high energy particle physics often rely on Monte Carlo simulations of signal and background. A problem with this approach is that it is not always possible to distinguish whether the machine is learning physics or simply an artefact of the simulation. In this presentation I will explain how it is possible to perform a new physics search with a...
  3. 11. Systematics aware learning: a case study in High Energy Physics
    Victor Estrade (LRI)
    17/10/2018, 09:50

    Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. The dataset used is a representative Higgs to tau tau analysis from ATLAS and released as part of the Kaggle Higgs ML challenge. Perturbations have been...
  4. 33. HiggsML challenge with optimized DNN
    Giles Chatham Strong (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
    17/10/2018, 10:15

    A lot of work done in advancing the performance of deep-learning approaches often takes place in the realms of image recognition - many papers use famous benchmark datasets, such as Cifar or Imagenet, to quantify the advantages their idea offers. However it is not always obvious, when reading such papers, whether the concepts presented can also be applied to problems in other domains and still...
  5. 23. The Lund Jet Plane
    Frederic Alexandre Dreyer (Oxford)
    17/10/2018, 11:10

    Lund diagrams, a representation of the phase space within jets, have long been used in discussing parton showers and resummations. We point out here that they can also serve as a powerful tool for experimentally characterising the radiation pattern within jets. We briefly comment on some of their analytical properties and highlight their scope for constraining Monte Carlo simulations. We then...
  6. 21. The TrackML tracking challenge : status report at half time
    Andreas Salzburger (CERN)
    17/10/2018, 11:35

    LHC experiments need to reconstruct the trajectory of particles from the few precise measurements in the detector. One major process is to « connect the dots », that is associate together the points left by each particle. The complexity of the process is growing exponentially with the LHC luminosity, so that new algorithms are needed. The TrackML challenge is a two phases competition to tackle...
  7. 47. HEP Machine Learning on HPCs
    Amir Farbin (University of Texas at Arlington (US))
    17/10/2018, 12:00

    Overview of current efforts and plans in HEP to use HPCs for Machine Learning.
  8. 31. DeepFlavour and Tensorflow in CMSSW
    Mauro Verzetti (CERN)
    17/10/2018, 12:25

    Jet classification, especially focused towards heavy-flavour jets, is of paramount importance for CMS. Modern ML techniques have been applied to this classification task resulting in a new generation of DeepLearning-based taggers that sport an improved performance. DeepFlavour, DeepAK8 and DeepDoubleB/C are the latest incarnations of these new family of classifiers that have been developed by...