Battat, J.B.R., Eldridge, C., Ezeribe, A.C. et al. (18 more authors) (2021) Improved sensitivity of the DRIFT-IId directional dark matter experiment using machine learning. Journal of Cosmology and Astroparticle Physics, 2021 (07). 014. ISSN 1475-7516
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (http://creativecommons.org/licenses/by/4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Keywords: | dark matter; directional; limits; machine learning; random forest classifier |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Mar 2021 07:18 |
Last Modified: | 22 Feb 2022 17:48 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1475-7516/2021/07/014 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172526 |
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Filename: Battat_2021_J._Cosmol._Astropart._Phys._2021_014.pdf
Licence: CC-BY 4.0