Semi-supervised Graph Neural Networks for Pileup Noise Removal

TL;DR

This paper introduces a semi-supervised graph neural network approach for pileup noise removal in high-energy physics, enabling effective training directly on experimental data without relying on simulation labels.

Contribution

It is the first to apply semi-supervised learning to pileup mitigation, improving performance and reducing dependence on simulation-based labels.

Findings

Outperforms traditional domain algorithms in pileup noise removal

Achieves comparable results to fully-supervised methods using simulation truth

Enables training directly on experimental data without ground truth

Abstract

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the ground truth information from simulation and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.