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

Improved sensitivity of the DRIFT-IId directional dark matter experiment using machine learning
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使用机器学习提高 DRIFT-IId 定向暗物质实验的灵敏度

DOI:
10.1088/1475-7516/2021/07/014
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发表时间:
2021
影响因子:
6.4
通讯作者:
Ingabire, I.
Ingabire, I.
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
Battat, J.B.R.;Eldridge, C.;Ezeribe, A.C.;Gaunt, O.P.;Gauvreau, J.-L.;Marcelo Gregorio, R.R.;Habich, E.K.K.;Hall, K.E.;Harton, J.L.;Ingabire, I.

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我们使用一种称为随机森林分类器的机器学习算法演示了一种新型的对漂移IID定向暗物质探测器的分析。该分析基于一系列选择参数将事件标记为信号或背景,而不是仅应用硬切割。分析效率在高能时与我们以前的结果相当,但在低能时效率更高。这导致了比15GeVc-2的WIMP质量低一个数量级的投影灵敏度增强,以及低至9GeVc-2的WIMP质量的投影灵敏度极限,这对于方向敏感的暗物质探测器来说是第一次。
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.
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DOI: 10.1088/1748-0221/9/11/p11004
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