Measuring the Gain of a Microchannel Plate/Phosphor Assembly Using a Convolutional Neural Network

Michael Jones; Matthew Harvey; William Bertsche; Andrew James Murray; Robert B. Appleby

doi:10.1109/TNS.2019.2950035

Measuring the Gain of a Microchannel Plate/Phosphor Assembly Using a Convolutional Neural Network

Michael Jones, Matthew Harvey, William Bertsche, Andrew James Murray, Robert B. Appleby

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Article number	8886588
Pages (from-to)	2430-2434
Number of pages	5
Journal	IEEE Transactions on Nuclear Science
Volume	66
Issue number	12
DOIs	https://doi.org/10.1109/TNS.2019.2950035
Publication status	Published - 29 Oct 2019
Externally published	Yes

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title = "Measuring the Gain of a Microchannel Plate/Phosphor Assembly Using a Convolutional Neural Network",

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.",

keywords = "micro-channel plate, phosphor screen, convolutional neural network, charge calibration, image intensity",

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AU - Jones, Michael

AU - Harvey, Matthew

AU - Bertsche, William

AU - Murray, Andrew James

AU - Appleby, Robert B.

PY - 2019/10/29

Y1 - 2019/10/29

N2 - 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.

AB - 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.

KW - micro-channel plate

KW - phosphor screen

KW - convolutional neural network

KW - charge calibration

KW - image intensity

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DO - 10.1109/TNS.2019.2950035

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EP - 2434

JO - IEEE Transactions on Nuclear Science

JF - IEEE Transactions on Nuclear Science

SN - 0018-9499

IS - 12

M1 - 8886588

ER -

Measuring the Gain of a Microchannel Plate/Phosphor Assembly Using a Convolutional Neural Network

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