An Orientation Selective Neural Network and its Application to Cosmic Muon Identification

TL;DR

This paper introduces an orientation selective neural network inspired by the visual cortex for identifying linear pixel patterns, successfully applied to cosmic muon detection in particle physics data with high accuracy and noise robustness.

Contribution

It presents a novel neural network architecture based on biological principles for linear pattern recognition, specifically applied to cosmic muon identification in detector data.

Findings

High accuracy in cosmic muon identification

Robust performance in noisy and inefficient pixel conditions

Suitable for fast parallel processing implementations

Abstract

We propose a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Following principles employed by the visual cortex, we employ orientation selective neurons in a neural network which performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons which leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The results compared with a visual scan point to a very satisfactory cosmic muon identification. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices.