ML4Jets2021

Jul 6 – 8, 2021
Europe/Zurich timezone

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

Jul 6, 2021, 9:40 AM
20m

Speaker

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

Description

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 typically require the evaluation of every possible permutation of these events to find the "best" assignment. In this work, we present a novel neural network architecture SPANet'', or Symmetry Preserving Attention networks. By casting this problem as a set assignment problem on a variable size set, and embedding our knowledge of the symmetries in the problem into the neural network architecture, we demonstrate that these problems can be solved efficiently even in cases for which existing methods are intractable. We demonstrate the approach using a suite of progressively more complex benchmarks, going from all-hadronic ttbar, via ttH, to 4top final states, and provide an easy to use and flexible software package to design and train networks for arbitrary final states.

Affiliation UCI Postdoc

Primary authors

Alex Shmakov (UCI) Michael James Fenton (University of California Irvine (US)) Pierre Baldi (UCI) Ta-Wei Ho (National Tsing Hua University (TW))

Presentation materials

 Fenton_ML4Jets2021.pdf Michael_Fenton.mp4