Author:
Pino N.,Agnes P.,Ahmad I.,Albergo S.,Albuquerque I.,Ave M.,Bonivento W. M.,Bottino B.,Cadeddu M.,Caminata A.,Canci N.,Cappello G.,Caravati M.,Catalanotti S.,Cataudella V.,Cesarano R.,Cicalò C.,Covone G.,de Candia A.,De Filippis G.,De Rosa G.,Dell’Aquila D.,Davini S.,Dionisi C.,Dolganov G.,Fiorillo G.,Franco D.,Galbiati C.,Gulino M.,Ippolito V.,Kemmerich N.,Kimura M.,Kuss M.,La Commara M.,Li X.,Mari S. M.,Martoff C. J.,Matteucci G.,Oleynikov V.,Pallavicini M.,Pandola L.,Rescigno M.,Rode J.,Sanfilippo S.,Sosa A.,Suvorov Y.,Testera G.,Tricomi A.,Wada M.,Wang H.,Wang Y.,Zakhary P.
Abstract
In the direct searches for Weakly Interacting Massive Particles (WIMPs) as Dark Matter candidates, the sensitivity of the detector to the incom- ing particle direction could provide a smoking gun signature for an interesting event. The SCENE collaboration firstly suggested the possible directional de- pendence of a dual-phase argon Time Projection Chamber through the columnar recombination effect. The Recoil Directionality project (ReD) within the Global Argon Dark Matter Collaboration aims to characterize the light and charge re- sponse of a liquid Argon dual-phase TPC to neutron-induced nuclear recoils to probe for the hint by SCENE. In this work, the directional sensitivity of the de- tector in the energy range of interest for WIMPs (20-100 keV) is investigated with a data-driven analysis involving a Machine Learning algorithm.