3rd IML Machine Learning Workshop

Session

Submitted contributions

17 Apr 2019, 14:00

500/1-001 - Main Auditorium (CERN)

Conveners

Submitted contributions: Session 4

• Rudiger Haake (Yale University (US))
• Steven Randolph Schramm (Universite de Geneve (CH))

27. A hybrid deep learning approach to vertexing

Henry Fredrick Schreiner (University of Cincinnati (US))

17/04/2019, 14:00

In the transition to Run 3 in 2021, LHCb will undergo a major luminosity upgrade, going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event, and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb...

48. Feature ranking based on subtraction methods

Paul Glaysher (DESY)

17/04/2019, 14:20

The input variables of ML methods in physics analysis are often highly correlated and figuring out which ones are the most important ones for the classification turns out to be a non-trivial tasks. We compare the standard method of TMVA to rank variables with a several newly developed methods based on iterative removal for the use case of a search for top pair associated Higgs production...

30. ML Techniques for heavy flavour identification in CMS

Emil Sorensen Bols (Vrije Universiteit Brussel (BE))

17/04/2019, 14:40

Jet flavour identification is a fundamental component for the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. Moreover, the classification of boosted jets has acquired an increasing importance in the physics program of CMS. In this presentation we will present the performance on both simulated and...

40. ParticleNet: Jet Tagging via Particle Clouds

Huilin Qu (Univ. of California Santa Barbara (US))

17/04/2019, 15:10

How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point cloud, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry....

23. Learning representations of irregular particle-detector geometry with distance-weighted graph networks

Jan Kieseler (CERN)

17/04/2019, 15:30

We explore the possibility of using graph networks to deal with irregular-geometry detectors when reconstructing particles. Thanks to their representation-learning capabilities, graph networks can exploit the detector granularity, while dealing with the event sparsity and the irregular detector geometry. In this context, we introduce two distance-weighted graph network architectures, the...

25. GroomRL: jet grooming through reinforcement learning

Frederic Alexandre Dreyer (Oxford)

17/04/2019, 16:30

We introduce a novel implementation of a reinforcement learning algorithm which is adapted to the problem of jet grooming, a crucial component of jet physics at hadron colliders. We show that the grooming policies trained using a Deep Q-Network model outperform state-of-the-art tools used at the LHC such as Recursive Soft Drop, allowing for improved resolution of the mass of boosted objects....

42. NeuralRinger: An Ensemble of Neural Networks Fed from Calorimeter Ring Sums for Triggering on Electrons

Werner Spolidoro Freund (Federal University of of Rio de Janeiro (BR))

17/04/2019, 16:50

In 2017, the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to reduce the latency of the first, fast, online software (HLT) selection stage for electrons with transverse energy above 15 GeV. In order to minimize detector response and shower development fluctuations, and being inspired in the ensemble of likelihood models currently operating in...

26. Fast Deep Learning on FPGAs for the Phase-II L0 Muon Barrel Trigger of the ATLAS Experiment

Luigi Sabetta (Sapienza Universita e INFN, Roma I (IT))

17/04/2019, 17:10

The Level-0 Muon Trigger system of the ATLAS experiment will undergo a full upgrade for HL-LHC to stand the challenging performances requested with the increasing instantaneous luminosity. The upgraded trigger system foresees to send RPC raw hit data to the off-detector trigger processors, where the trigger algorithms run on new generation of Field-Programmable Gate Arrays (FPGAs). The FPGA...

20. Close-out

Steven Randolph Schramm (Universite de Geneve (CH))

17/04/2019, 17:30

3. Coffee break

66. Talk 1

67. Talk 2

68. Talk 3

69. Talk 4

70. Talk 5

71. Talk 6

72. Talk 7

73. Talk 8

78. Talk 9

32. Towards Fast Displaced Vertex Finding

Kim Albertsson (Lulea University of Technology (SE))

Many standard model extensions predict long-lived massive particles that can be detected by looking for displaced decay vertices in the inner detector volume. Current approaches to seek for these events in high-energy particle collisions rely on the presence of additional energetic signatures to make an online selection during data-taking. Enabling trigger-level reconstruction of displaced...