Unsupervised Particle Tracking with Neuromorphic Computing

• URL: http://arxiv.org/abs/2502.06771v1
• Date: Mon, 10 Feb 2025 18:50:50 GMT
• Title: Unsupervised Particle Tracking with Neuromorphic Computing
• Authors: Emanuele Coradin, Fabio Cufino, Muhammad Awais, Tommaso Dorigo, Enrico Lupi, Eleonora Porcu, Jinu Raj, Fredrik Sandin, Mia Tosi,
• Abstract summary: We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments.
• Score: 1.7471104495490826
• License:
• Abstract: We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase II detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments.

Related papers

• Machine Learning for Anomaly Detection in Particle Physics [0.0]
  The detection of out-of-distribution data points is a common task in particle physics. Recent advances in Machine Learning for anomaly detection have encouraged the utilization of such techniques on particle physics problems. This review article provides an overview of the state-of-the-art techniques for anomaly detection in particle physics using machine learning.
  arXiv  Detail & Related papers  (2023-12-20T12:40:00Z)
• Designing Observables for Measurements with Deep Learning [0.12277343096128711]
  We propose to design targeted observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest. We demonstrate this idea in simulation using two physics models for inclusive measurements in deep in scattering.
  arXiv  Detail & Related papers  (2023-10-12T20:54:34Z)
• A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science [0.0]
  The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. The model is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing.
  arXiv  Detail & Related papers  (2023-06-03T03:36:47Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [124.29621071934693]
  We present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. TransformerCVN simultaneously classifies each event and reconstructs every individual particle's identity. This architecture enables us to perform several interpretability studies which provide insights into the network's predictions.
  arXiv  Detail & Related papers  (2023-03-10T20:36:23Z)
• Neural network enhanced measurement efficiency for molecular groundstates [63.36515347329037]
  We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians. We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
  arXiv  Detail & Related papers  (2022-06-30T17:45:05Z)
• NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields [65.07940731309856]
  Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. In this paper, we consider a partially observable scenario known as fluid dynamics grounding. We propose a differentiable two-stage network named NeuroFluid. It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities.
  arXiv  Detail & Related papers  (2022-03-03T15:13:29Z)
• Spatiotemporal Spike-Pattern Selectivity in Single Mixed-Signal Neurons with Balanced Synapses [0.27998963147546135]
  Mixed-signal neuromorphic processors could be used for inference and learning. We show how inhomogeneous synaptic circuits could be utilized for resource-efficient implementation of network layers.
  arXiv  Detail & Related papers  (2021-06-10T12:04:03Z)
• Quantum speedup for track reconstruction in particle accelerators [51.00143435208596]
  We identify four fundamental routines present in every local tracking method and analyse how they scale in the context of a standard tracking algorithm. Although the found quantum speedups are mild, this constitutes to the best of our knowledge, the first rigorous evidence of a quantum advantage for a high-energy physics data processing task.
  arXiv  Detail & Related papers  (2021-04-23T13:32:14Z)
• Learning physical properties of anomalous random walks using graph neural networks [0.0]
  Single particle tracking allows probing how biomolecules interact physically with their natural environments. Reliable inference is made difficult by the inherent nature of single particle motion, by experimental noise, and by the short duration of most experimental trajectories. Here, we introduce a new, fast approach to inferring random walk properties based on graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2021-03-22T11:32:10Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.