# Measuring the Gain of a Micro-Channel Plate/Phosphor Assembly Using a Convolutional Neural Network

## TL;DR

This paper introduces a neural network-based method to accurately measure the gain of a micro-channel plate/phosphor assembly by analyzing phosphor screen images, enabling precise gain curve determination.

## Contribution

It presents a novel application of convolutional neural networks to analyze MCP/phosphor images and extract gain measurements, improving noise reduction and measurement accuracy.

## Key findings

- Neural network effectively denoises phosphor images.
- Accurate single-electron intensity measurement achieved.
- Gain curve of MCP determined from image analysis.

## Abstract

This paper presents a technique to measure the gain of a single-plate micro-channel plate (MCP)/phosphor assembly by using a convolutional neural network to analyse images of the phosphor screen, recorded by a charge coupled device. The neural network reduces the background noise in the images sufficiently that individual electron events can be identified. From the denoised images, an algorithm determines the average intensity recorded on the phosphor associated with a single electron hitting the MCP. From this average single-particle-intensity, along with measurements of the charge of bunches after amplification by the MCP, we were able to deduce the gain curve of the MCP.