2nd IML Machine Learning Workshop

Session

Session 5

11 Apr 2018, 09:00

500/1-001 - Main Auditorium (CERN)

Conveners

Session 5

• Paul Seyfert (CERN)
• Rudiger Haake (CERN)

35. Daily announcements

Lorenzo Moneta (CERN), Markus Stoye (CERN), Paul Seyfert (CERN), Rudiger Haake (CERN), Steven Randolph Schramm (Universite de Geneve (CH))

11/04/2018, 09:00

16. Fisher information metrics for binary classifier evaluation and training

Andrea Valassi (CERN)

11/04/2018, 09:05

Different evaluation metrics for binary classifiers are appropriate to different scientific domains and even to different problems within the same domain. This presentation focuses on the optimisation of event selection to minimise statistical errors in HEP parameter estimation, a problem that is best analysed in terms of the maximisation of Fisher information about the measured parameters....

25. DeepJet: a portable ML environment for HEP

Swapneel Sundeep Mehta (Dwarkadas J Sanghvi College of Engineering (IN)), Mr swapneel mehta (IT/DB Group)

11/04/2018, 09:30

In this presentation we will detail the evolution of the DeepJet python environment. Initially envisaged to support the development of the namesake jet flavour tagger in CMS, DeepJet has grown to encompass multiple purposes within the collaboration. The presentation will describe the major features the environment sports: simple out-of-memory training with a multi-treaded approach to maximally...

13. Event Categorization using Deep Neural Networks for ttH (H→bb) with the CMS Experiment

Marcel Rieger (RWTH Aachen University (DE))

11/04/2018, 09:55

The analysis of top-quark pair associated Higgs boson production enables a direct measurement of the top-Higgs Yukawa coupling. In ttH (H→bb) analyses, multiple event categories are commonly used in order to simultaneously constrain signal and background contributions during a fit to data. A typical approach is to categorize events according to both their jet and b-tag multiplicities. The...

27. Machine learning in jet physics

Sreedevi Narayana Varma (King's College London)

11/04/2018, 11:00

High energy collider experiments produce several petabytes of data every year. Given the magnitude and complexity of the raw data, machine learning algorithms provide the best available platform to transform and analyse these data to obtain valuable insights to understand Standard Model and Beyond Standard Model theories. These collider experiments produce both quark and gluon initiated...

20. Recursive Neural Networks in Quark/Gluon Tagging

Taoli Cheng (University of Chinese Academy of Sciences)

11/04/2018, 11:25

Vidyo contribution

Based on the natural tree-like structure of jet sequential clustering, the recursive neural networks (RecNNs) embed jet clustering history recursively as in natural language processing. We explore the performance of RecNN in quark/gluon discrimination. The results show that RecNNs work better than the baseline BDT by a few percent in gluon rejection at the working point of...

19. Deep neural network-based multi-class boosted object tagger in ATLAS

Ece Akilli (Universite de Geneve (CH))

A deep neural network-based multi-class boosted object tagger is developed in the context of a search for pair production of heavy vector-like quarks with hadronic final states in ATLAS. The four classes of the tagger are W/Z (V)-boson, Higgs-boson, top-quark and background jets. As the unambiguous identification of the origin of the jet is essential for this search, an identification...

3. Training Deep Learning Models on Many-Core Processors

Mr David Nonso Ojika (University of Florida (US))

Leveraging on our previous work on developing DNN-based classification models for Higss events [1], we turn to CNN-based classification models for muon events. Using Intel Knights Landing (KNL) processors, we present performance metrics on training convolutional neural networks (CNNs) on multiple KNL computing nodes for the task of muon identification (i.e "high Pt" or "low Pt"). This work is...