Improved Sensitivity of the DRIFT-IId Directional Dark Matter Experiment using Machine Learning

11 Mar 2021 · J. B. R. Battat, C. Eldridge, A. C. Ezeribe, O. P. Gaunt, J. -L. Gauvreau, R. R. Marcelo Gregorio, E. K. K. Habich, K. E. Hall, J. L. Harton, I. Ingabire, R. Lafler, D. Loomba, W. A. Lynch, S. M. Paling, A. Y. Pan, A. Scarff, F. G. Schuckman II, D. P. Snowden-Ifft, N. J. C. Spooner, C. Toth, A. A. Xu ·

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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Improved Sensitivity of the DRIFT-IId Directional Dark Matter Experiment using Machine Learning

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