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)
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...
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...
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...
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...
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...
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...
