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Ising noise filter: physics-informed filtering for particle detectors

I. Kharuk

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.

Ising noise filter: physics-informed filtering for particle detectors

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.
Paper Structure (9 sections, 15 equations, 6 figures, 1 table)

This paper contains 9 sections, 15 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Neutrino telescopes: Baikal-GVD
  3. Detector and noise environment
  4. Mapping event to Ising model
  5. Accelerator physics: SPD at NICA
  6. Detector and noise environment
  7. Ising filtering for SPD
  8. Track finding with the Peterson--Hopfield network
  9. Discussion

Figures (6)

  • Figure 1: Baikal-GVD design.
  • Figure 2: Illustration of neutrino-induced event topologies.
  • Figure 3: Illustration of applying Ising filter for noise filtering on Baikal-GVD data. Marker size for signal hits is proportional to the charge registered by OM.
  • Figure 4: SPD detector design.
  • Figure 5: Illustration of Ising filter for the SPD detector. Tracks are denoted according to ground truth Monte Carlo data.
  • ...and 1 more figures