ML4Jets2021

Session

New architectures

6 Jul 2021, 09:00

Conveners

New architectures: Equivariance / Invariance

• Barry Dillon (University of Heidelberg)
• Matthew Dolan (University of Melbourne)

New architectures: New Strategies or Representations

• Barry Dillon (University of Heidelberg)
• Matthew Dolan (University of Melbourne)

86. SO(3)-equivariant Neural Network for b-tagging

Ema Catalina Smith

06/07/2021, 09:00

Jets originating from bottom quarks, b-jets, are of particular interest in high energy physics. While b-jets are similar to other jets, they have certain qualities that present unique challenges in the context of machine learning. Generally, there is an underlying rotational symmetry of the particles about a jet’s axis. However, in the case of b-jets, some of the most discriminating...

69. Equivariant energy flow networks for jet tagging

Ayodele Ore (The University of Melbourne)

06/07/2021, 09:20

The Energy Flow Network (EFN) is a neural network architecture that represents jets as point clouds and enforces infrared and collinear (IRC) safety on its outputs. In this talk, I will introduce a new variant of the EFN architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. I will discuss the conditions under which IRC safety can be maintained in the new...

49. SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention

Michael James Fenton (University of California Irvine (US))

06/07/2021, 09:40

One of the most ubiquitous challenges in analyses at the LHC is event reconstruction, whereby heavy resonance particles (such as top quarks, Higgs bosons, or vector bosons) must be reconstructed from the detector signatures left behind by their decay products. This is particularly challenging when all decay products have similar or identical signatures, such as all-jet events. Existing methods...

89. Particle Convolution for Jets

Chase Owen Shimmin (Yale University (US))

06/07/2021, 10:00

We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics. The particle convolution layer can be viewed as an extension of Deep Sets and Energy Flow network architectures, in which the permutation-invariant operator is promoted to a group convolution. While the PCN can be implemented for various kinds of...

14. Linearized Optimal Transport for Jet Physics

Ms Tianji Cai (Department of Physics, University of California, Santa Barbara)

06/07/2021, 10:20

Optimal Transport has been applied to jet physics for the computation of distance between collider events. Here we generalize the Energy Mover’s Distance to include both the balanced Wasserstein-2 (W2) distance and the unbalanced Hellinger-Kantorovich (HK) distance. Whereas the W2 distance only allows for mass to be transported, the HK distance allows mass to be transported, created and...

22. Supervised Attention for Jet Classification

Jonathan Shlomi (Weizmann Institute of Science (IL))

06/07/2021, 10:40

Secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve classification performance. This can be thought of as a supervised attention mechanism - directing the classifier towards the relevant information inside the jet. We show supervised attention outperforms an identical...

84. The information content of quenched jets

James Mulligan (University of California, Berkeley (US))

06/07/2021, 11:00

In high energy heavy-ion collisions the substructure of jets is modified compared to that in proton-proton collisions due to the presence of the quark-gluon plasma (QGP). This modification of jets in the QGP is called ''jet quenching''. We employ machine learning techniques to quantify how much information about this process is within the substructure observables. We formulate the question as...

85. Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors

Jitka Mrazkova

06/07/2021, 11:20

Jets of collimated particles originating from hard scattered partons are utilized in a wide range of analyses in high energy physics. Our study is focused on identifying jets originating from heavy quarks. We introduce a novel approach to tagging heavy-flavor jets at collider experiments utilizing the information contained within jet constituents via the JetVLAD model architecture. This model...

40. A new approach to unsupervised learning in jet physics

Peter Rangi Sorrenson (Universität Heidelberg)

06/07/2021, 11:40

TBC