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IML Machine Learning Workshop

20–22 Mar 2017
CERN
Europe/Zurich timezone
There is a live webcast for this event.

Contribution List

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  1. 23. Introduction to the workshop
    Sergei Gleyzer (University of Florida (US)), Lorenzo Moneta (CERN), Michele Floris (CERN), Paul Seyfert (Universita & INFN, Milano-Bicocca (IT)), Steven Randolph Schramm (Universite de Geneve (CH))
    20/03/2017, 09:00
  2. 25. Introduction to the Industry Session on ML
    20/03/2017, 09:30
  3. 26. Nvidia and ML
    Piero Altoe
    20/03/2017, 09:50
  4. 28. Yandex and ML
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
    20/03/2017, 10:15
  5. 38. Announcements
    20/03/2017, 10:39
  6. 31. Intel and ML
    Hans Pabst (Intel Corp.)
    20/03/2017, 11:15
  7. 32. IBM and ML
    Graham Mackintosh
    20/03/2017, 11:40
  8. 24. Industry-HEP panel on Machine Learning
    20/03/2017, 12:05
  9. 33. Introduction to the session, CWP
    Sergei Gleyzer (University of Florida (US))
    20/03/2017, 14:00
  10. 21. CWP discussion part 1
    20/03/2017, 14:15
  11. 22. CWP discussion part 2
    20/03/2017, 16:30
  12. 1. Session introduction
    21/03/2017, 09:00
  13. 2. Jet reconstruction and heavy jet tagging at LHCb
    Lorenzo Sestini (Universita e INFN, Padova (IT))
    21/03/2017, 09:05

    The jet reconstruction and the heavy jet flavour tagging at LHCb will be discussed with focus on the last published measurements such as the measurement of forward tt, W+bb and W+cc production in pp collisions at √s=8 TeV and the search for the SM Higgs boson decaying in bbbar or ccbar in association to W or Z boson.

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  14. 3. Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment
    Daniel Hay Guest (University of California Irvine (US))
    21/03/2017, 09:45

    A novel b-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS Experiment. This talk presents the expected performance of the RNN based b-tagging in simulated $t \bar t$ events. The RNN based b-tagging processes properties of tracks associated to jets which are represented in sequences. In contrast to traditional impact-parameter-based b-tagging...

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  15. 4. Exploring neural networks to improve b-jet tagging with the ALICE detector
    Rudiger Haake (CERN)
    21/03/2017, 10:10

    Highly energetic jets are sensitive probes for the kinematics and the topology of nuclear collisions. Jets are collimated sprays of charged and neutral particles, which are produced in the fragmentation of hard scattered partons in an early stage of the collision. Heavy-quark jets, originating from beauty or charm quarks (b- and c-jets), are particularly good probes to shed light on the...

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  16. 5. DeepFlavour in CMS
    Markus Stoye (CERN)
    21/03/2017, 10:35

    Flavour-tagging of jets is an important task in collider based high energy physics and a field where machine learning tools are applied by all major experiments. A new tagger (DeepFlavour) was developed and commissioned in CMS that is based on an advanced machine learning procedure. A deep neural network is used to do multi-classification of jets that origin from a b-quark, two b-quarks, a...

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  17. 6. Flavor Tagging with Deep Neural Networks at Belle II
    Jochen Gemmler (KIT/IEKP)
    21/03/2017, 11:30

    The Belle II experiment is mainly designed to investigate the decay of B meson pairs from $\Upsilon(4S)$ decays, produced by the asymmetric electron-positron collider SuperKEKB. The determination of the B meson flavor, so-called flavor tagging, plays an important role in analyses and can be inferred in many cases directly from the final state particles. In this talk a successful approach of...

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  18. 7. Inclusive tagging of B-flavour at LHCb [Vidyo]
    Aleksei Rogozhnikov (Yandex School of Data Analysis (RU))
    21/03/2017, 11:55

    One of the most important procedure needed for the study of CP violation in Beauty sector is the tagging of the flavour of neutral B-mesons at production. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present a proposal to upgrade current flavour tagging strategy in LHCb experiment. This strategy consists of inclusive tagging ensemble...

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  19. 8. Quark/gluon jet discrimination: a reproducible analysis using R
    Dr Andrew Lowe (Hungarian Academy of Sciences (HU))
    21/03/2017, 12:20

    The power to discriminate between light-quark jets and gluon jets would have a huge impact on many searches for new physics at CERN and beyond. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between quark and gluon jets at experiments like those at the Large Hadron Collider at CERN. A new fast feature selection method...

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  20. 34. Tutorial 1: TMVA
    Lorenzo Moneta (CERN), Sergei Gleyzer (University of Florida (US))
    21/03/2017, 14:00
  21. 35. Tutorial 2: Scikit-learn
    Gilles Louppe (New York University (US))
    21/03/2017, 15:00
  22. 9. Session introduction
    22/03/2017, 09:00
  23. 10. Identification of Hadronically Decaying W Bosons and Top Quarks Using High-Level Features as Input to Boosted Decision Trees and Deep Neural Networks in ATLAS at 13 TeV
    Ece Akilli (Universite de Geneve (CH))
    22/03/2017, 09:05

    http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2017-004/

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  24. 11. Deep-learning Top Taggers or The End of QCD?
    Gregor Kasieczka (Eidgenoessische Technische Hochschule Zuerich (CH))
    22/03/2017, 09:45

    https://arxiv.org/abs/1701.08784

    Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model...

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  25. 12. Top tagging with deep neural networks [Vidyo]
    Jannicke Pearkes (University of British Columbia (CA))
    22/03/2017, 10:10

    Recent literature on deep neural networks for top tagging has focussed on image based techniques or multivariate approaches using high level jet substructure variables. Here, we take a sequential approach to this task by using anordered sequence of energy deposits as training inputs. Unlike previous approaches, this strategy does not result in a loss of information during pixelization or the...

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  26. 13. Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
    Chase Owen Shimmin (Yale University (US))
    22/03/2017, 10:35

    We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers. The network is trained...

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  27. 14. Deep Convolutional Networks for Event Reconstruction and Particle Tagging on NOvA and DUNE
    Fernanda Psihas (Indiana University)
    22/03/2017, 11:30

    Deep Convolutional Neural Networks (CNNs) have been widely applied in computer vision to solve complex problems in image recognition and analysis. In recent years many efforts have emerged to extend the use of this technology to HEP applications, including the Convolutional Visual Network (CVN), our implementation for identification of neutrino events. In this presentation I will describe the...

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  28. 16. Application of Generative Adversarial Networks (GANs) to jet images
    Michela Paganini (Yale University (US))
    22/03/2017, 12:10

    https://arxiv.org/abs/1701.05927

    We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple...

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  29. 15. Using Boosted Decision Trees to look for displaced Jets in the ATLAS Calorimeter
    Gordon Watts (University of Washington (US))
    22/03/2017, 12:35

    A boosted decision tree is used to identify unique jets in a recently released conference note describing a search for long lived particles decaying to hadrons in the ATLAS Calorimeter. Neutral Long lived particles decaying to hadrons are “typical” signatures in a lot of models including Hidden Valley models, Higgs Portal Models, Baryogenesis, Stealth SUSY, etc. Long lived neutral particles...

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  30. 36. Tutorial 3: Keras and TMVA interfaces
    Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE))
    22/03/2017, 14:00
  31. 37. Tutorial 4: R
    Andrew Lowe (Hungarian Academy of Sciences (HU))
    22/03/2017, 15:00
  32. 17. HEP.TrkX [Vidyo]
    Dustin James Anderson (California Institute of Technology (US))
    22/03/2017, 16:30

    Reconstruction of charged particle tracks is a central task in the processing of physics data at the LHC and other colliders. Current state-of-the-art tracking algorithms are based on the Kalman filter and have seen great success both offline and at trigger level. However, these algorithms scale poorly with increasing detector occupancy, and it is foreseen that significant changes will be...

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  33. 18. Object identification with deep learning using Intel DAAL on Knights Landing processor [Vidyo]
    David Nonso Ojika (University of Florida (US))
    22/03/2017, 17:05

    The problem of object recognition is computationally expensive, especially when large amounts of data is involved. Recently, techniques in deep neural networks (DNN) - including convolutional neural networks and residual neural networks - have shown great recognition accuracy compared to traditional methods (artificial neural networks, decision tress, etc.). However, experience reveals that...

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  34. 20. Close-out
    Steven Randolph Schramm (Universite de Geneve (CH))
    22/03/2017, 17:30
  35. 29. CERN Openlab and ML
    Maria Girone (CERN)
  36. 19. Challenge results
  37. 30. Industry ML Talk 4