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

by Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

Submission summary

As Contributors: Michael James Fenton
Arxiv Link: (pdf)
Code repository:
Data repository:
Date submitted: 2021-12-24 21:44
Submitted by: Fenton, Michael James
Submitted to: SciPost Physics
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology
Approaches: Experimental, Computational


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

Current status:
Editor-in-charge assigned

Author comments upon resubmission

Resubmission to address minor editorial comments from reviewer

List of changes

Added short discussion of MEM from CMS ttH analysis

Submission & Refereeing History

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Resubmission 2106.03898v3 on 24 December 2021

Reports on this Submission

Anonymous Report 1 on 2022-1-4 (Invited Report)


I thank the authors for the additional clarifications and changes to the manuscript and support publication in the current form.

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