3rd IML Machine Learning Workshop

Session

Submitted contributions

16 Apr 2019, 14:00

500/1-001 - Main Auditorium (CERN)

Conveners

Submitted contributions: Session 2

• Steven Randolph Schramm (Universite de Geneve (CH))
• David Rousseau (LAL-Orsay, FR)

21. Novelty Detection Meets Collider Physics

Ms Ying-Ying Li (HKUST)

16/04/2019, 14:00

Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to...

31. Uncertain Networks

Jennifer Thompson (ITP Heidelberg)

16/04/2019, 14:30

Machine learning methods are being increasingly and successfully applied to many different physics problems. However, currently uncertainties in machine learning methods are not modelled well, if at all. In this talk I will discuss how using Bayesian neural networks can give us a handle on uncertainties in machine learning. I will use tagging tops vs. QCD as an example of how these networks...

28. Exploring SMEFT in VH channel with Machine Learning

Charanjit Kaur Khosa

16/04/2019, 15:00

We use Machine Learning(ML) techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the SMEFT framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the...

24. The Tracking Machine Learning challenge

David Rousseau (LAL-Orsay, FR)

16/04/2019, 15:30

The HL-LHC will see ATLAS and CMS see proton bunch collisions reaching track multiplicity up to 10.000 charged tracks per event. Algorithms need to be developed to harness the increased combinatorial complexity. To engage the Computer Science community to contribute new ideas, we have organized a Tracking Machine Learning challenge (TrackML). Participants were provided events with 100k 3D...

55. Talk 1

56. Talk 2

57. Talk 3

58. Talk 4

59. Talk 5