Ising noise filter: physics-informed filtering for particle detectors

TL;DR

The paper introduces the Ising noise filter, a physics-informed, graph-based pre-filtering algorithm that effectively suppresses background noise in particle detectors, improving detection accuracy and computational efficiency.

Contribution

The Ising noise filter is a novel, portable, graph-based algorithm that leverages physics-informed interaction kernels to enhance noise rejection in particle detection systems.

Findings

Achieves 96.8% recall for astrophysical neutrinos in Baikal-GVD

Attains 97% recall on toy Monte Carlo for NICA SPD detector

Improves TrackML score from 0.5 to 0.95 when combined with track finding networks

Abstract

We present the Ising noise filter, a highly portable, graph-based pre-filtering algorithm for early-stage background suppression in particle accelerators and astrophysical detectors. Standard noise rejection methods relying on track fitting suffer from severe combinatorial explosion. Our method bypasses this by mapping individual detector hits to a network of binary spins and minimizing an energy functional. The interaction kernels are physics-informed, tailored to the underlying physics and geometry of the experiment. We demonstrate the efficacy of this approach in two distinct experimental regimes. Applied to the Baikal-GVD neutrino telescope the filter yields fast, standard-quality noise rejection with 96.8\% recall for astrophysical neutrinos. For the SPD detector at the NICA collider the filter attains recall of 97\% on a toy Monte Carlo sample. Furthermore, when combined with a Peterson--Hopfield network for track finding, our physics-informed coupling improves the TrackML score from 0.5 to 0.95.