ML4Jets2020

Session

Architectures

15 Jan 2020, 11:10

KC 802 (Kimmel Center for University Life)

Conveners

Architectures

• Matthew Schwartz
• Taoli Cheng (University of Montreal)

9. CapsNets Continuing the Convolutional Quest

Sascha Daniel Diefenbacher (Hamburg University (DE))

15/01/2020, 11:10

Convolutional Neural Networks are an important tool for image classification both in and outside of particle physics. Capsule networks allow us to expand on the standard CNNs setup, both to increase the networks performance and to give insight into its decision making processes. We demonstrate the use of the Capsule Networks by separating a resonance decaying to top quarks from both, QCD...

24. Quark-gluon discrimination with point clouds

Vinicius Massami Mikuni (Universitaet Zuerich (CH))

15/01/2020, 11:30

Quark-gluon tagging refers to the task of identifying the origin of a jet as produced from the hadronization of a gluon or a quark. Common methods rely on jet constituent properties to disentangle the two objects to varying degrees of success. In this talk an innovative method of classifying jets according to its constituents is introduced. The method uses the information of the constituents...

41. Modeling a top jet classifier with two-point energy correlation and geometry of soft emission

Sung Hak Lim (KEK)

15/01/2020, 11:50

We introduce a two-point energy correlation spectra analysis for the classification of top jets and QCD jets. The two-point energy correlation spectra based on the angle between constituents, which is the main parameter of the kinematics of parton shower and heavy particle decay, are useful for tagging Higgs jets with a multilayer perceptron (MLP) or logistic regression. On the other hand, the...

10. Deep Learning Jet Substructure from Two-Particle Correlation

Dr Yang-Ting Chien (Stony Brook University)

15/01/2020, 12:10

Deciphering the complex information contained in jets produced in collider events requires a physical organization of the jet data. In this talk I will discuss the use of two-particle correlations (2PCs) by pairing individual particles as the initial jet representation from which a probabilistic model can be built. Particle momenta, as well as particle types and vertex information are included...

53. CLARIANT: Covariant Lorentz Group Architecture for Artificial Neural Networks

Alexander Bogatskiy

15/01/2020, 12:30

We present a new neural network architecture, \NetworkName: a Lorentz covariant neural network architecture for learning the kinematics and properties of complex systems of particles. The novel design of this network implements activations as vectors that transform according to arbitrary finite-dimensional representations of the underlying symmetry group that governs particle physics, the...