DeepAI AI Chat
Log In Sign Up

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

06/07/2021
by   Alexander Shmakov, et al.
0

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are avaiable at https://github.com/Alexanders101/SPANet

READ FULL TEXT

page 1

page 2

page 3

page 4

10/19/2020

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

Top quarks are the most massive particle in the Standard Model and are p...
03/24/2023

Topological Reconstruction of Particle Physics Processes using Graph Neural Networks

We present a new approach, the Topograph, which reconstructs underlying ...
01/29/2023

PhyCV: The First Physics-inspired Computer Vision Library

PhyCV is the first computer vision library which utilizes algorithms dir...
12/20/2016

Exploring Different Dimensions of Attention for Uncertainty Detection

Neural networks with attention have proven effective for many natural la...
12/10/2017

Gradient Normalization & Depth Based Decay For Deep Learning

In this paper we introduce a novel method of gradient normalization and ...
02/02/2017

QCD-Aware Recursive Neural Networks for Jet Physics

Recent progress in applying machine learning for jet physics has been bu...