Author:
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.,Lafler R.,Loomba D.,Lynch W.A.,Paling S.M.,Pan A.Y.,Scarff A.,Schuckman II F.G.,Snowden-Ifft D.P.,Spooner N.J.C.,Toth C.,Xu A.A.
Abstract
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.
Subject
Astronomy and Astrophysics
Cited by
2 articles.
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