Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
SciPost Phys. 12, 178 (2022) ·
published 30 May 2022
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· pdf
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
Dr Fenton: "We have edited the baseline di..."
in Submissions | report on SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention