Engineering topics

Paling, S. M.

Publications and source records attributed to Paling, S. M..

3 records

DOE OSTI2026

Design and development of optical modules for the BUTTON-30 detector

BUTTON-30 is a neutrino detector demonstrator located in the STFC Boulby underground facility in the north-east of England. The main goal of the project is to deploy and test the performance of the gadolinium-loaded water-based liquid scintillator for neutrino detection in an underground environment. This will pave the way for a future large-volume neutrino observatory that can also perform remote monitoring of nuclear reactors for nonproliferation. This paper describes the design and construction of the watertight optical modules of the experiment.

COMSOL simulations↗

DOE OSTI2023

Acceptance tests of Hamamatsu R7081 photomultiplier tubes

Photomultiplier tubes (PMTs) are traditionally an integral part of large underground experiments as they measure the light emission from particle interactions within the enclosed detection media. The BUTTON experiment will utilise around 100 PMTs to measure the response of different media suitable for rare event searches. A subset of low-radioactivity 10-inch Hamamatsu R7081 PMTs were tested, characterised, and compared to manufacture certification. This manuscript describes the laboratory tests and analysis of gain, peak-to-valley ratio and dark rate of the PMTs to give an understanding of the charge response, signal-to-noise ratio and dark noise background as an acceptance test of the suitability of these PMTs for water-based detectors. Following the evaluation of these tests, the PMT performance agreed with the manufacturer specifications. These results are imperative for modeling the PMT response in detector simulations and providing confidence in the performance of the devices once installed in the detector underground.

neutrino detectors↗

DOE OSTI2021

Improved sensitivity of the DRIFT-IId directional dark matter experiment using machine learning

We demonstrate a new type of analysis for the DRIFT-IId directional dark matter detector using a machine learning algorithm called a Random Forest Classifier. The analysis labels events as signal or background based on a series of selection parameters, rather than solely applying hard cuts. The analysis efficiency is shown to be comparable to our previous result at high energy but with increased efficiency at lower energies. This leads to a projected sensitivity enhancement of one order of magnitude below a WIMP mass of 15 GeV c -2 and a projected sensitivity limit that reaches down to a WIMP mass of 9 GeV c -2 , which is a first for a directionally sensitive dark matter detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗