Simulation of charge readout with segmented tiles in nEXO
Creators: name: Li, Z.
name: Cen, W. R.
name: Robinson, A.
name: Moore, D. C.
name: Wen, L. J.
name: Odian, A.
name: Kharusi, S. Al
name: Anton, G.
name: Arnquist, I. J.
name: Badhrees, I.
name: Barbeau, P. S.
name: Beck, D.
name: Belov, V.
name: Bhatta, T.
name: Brodsky, J. P.
name: Brown, E.
name: Brunner, T.
name: Caden, E.
name: Cao, G. F.
name: Cao, L.
name: Chambers, C.
name: Chana, B.
name: Charlebois, S. A.
name: Chiu, M.
name: Cleveland, B.
name: Coon, M.
name: Craycraft, A.
name: Dalmasson, J.
name: Daniels, T.
name: Darroch, L.
name: Daugherty, S. J.
name: Croix, A. De St.
name: Der Mesrobian-Kabakian, A.
name: DeVoe, R.
name: Di Vacri, M. L.
name: Dilling, J.
name: Ding, Y. Y.
name: Dolinski, M. J.
name: Dragone, A.
name: Echevers, J.
name: Elbeltagi, M.
name: Fabris, L.
name: Fairbank, D.
name: Fairbank, W.
name: Farine, J.
name: Ferrara, S.
name: Feyzbakhsh, S.
name: Fontaine, R.
name: Fucarino, A.
name: Gallina, G.
name: Gautam, P.
name: Giacomini, G.
name: Goeldi, D.
name: Gornea, R.
name: Gratta, G.
name: Hansen, E. V.
name: Heffner, M.
name: Hoppe, E. W.
name: Hößl, J.
name: House, A.
name: Hughes, M.
name: Iverson, A.
name: Jamil, A.
name: Jewell, M. J.
name: Jiang, X. S.
name: Karelin, A.
name: Kaufman, L. J.
name: Kodroff, D.
name: Koffas, T.
name: Krücken, R.
name: Kuchenkov, A.
name: Kumar, K. S.
name: Lan, Y.
name: Larson, A.
name: Leach, K. G.
name: Lenardo, B. G.
name: Leonard, D. S.
name: Li, G.
name: Li, S.
name: Licciardi, C.
name: Lin, Y. H.
name: Lv, P.
name: MacLellan, R.
name: McElroy, T.
name: Medina-Peregrina, M.
name: Michel, T.
name: Mong, B.
name: Murray, K.
name: Nakarmi, P.
name: Natzke, C. R.
name: Newby, R. J.
name: Ning, Z.
name: Njoya, O.
name: Nolet, F.
name: Nusair, O.
name: Odgers, K.
name: Oriunno, M.
name: Orrell, J. L.
name: Ortega, G. S.
name: Ostrovskiy, I.
name: Overman, C. T.
name: Parent, S.
name: Piepke, A.
name: Pocar, A.
name: Pratte, J. -F.
name: Radeka, V.
name: Raguzin, E.
name: Rescia, S.
name: Retière, F.
name: Richman, M.
name: Rossignol, T.
name: Rowson, P. C.
name: Roy, N.
name: Runge, J.
name: Saldanha, R.
name: Sangiorgio, S.
name: VIII, K. Skarpaas
name: Soma, A. K.
name: St-Hilaire, G.
name: Stekhanov, V.
name: Stiegler, T.
name: Sun, X. L.
name: Tarka, M.
name: Todd, J.
name: Tolba, T.
name: Totev, T. I.
name: Tsang, R.
name: Tsang, T.
name: Vachon, F.
name: Veeraraghavan, V.
name: Viel, S.
name: Visser, G.
name: Vivo-Vilches, C.
name: Vuilleumier, J. -L.
name: Wagenpfeil, M.
name: Walent, M.
name: Wang, Q.
name: Ward, M.
name: Watkins, J.
name: Weber, M.
name: Wei, W.
name: Wichoski, U.
name: Wu, S. X.
name: Wu, W. H.
name: Wu, X.
name: Xia, Q.
name: Yang, H.
name: Yang, L.
name: Yen, Y. -R.
name: Zeldovich, O.
name: Zhao, J.
name: Zhou, Y.
name: Ziegler, T.
Publisher: name: arXiv
Year: 2019
Subjects: subject: Instrumentation and Detectors (physics.ins-det)
subject: Nuclear Experiment (nucl-ex)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: nEXO is a proposed experiment to search for the neutrino-less double beta decay ($0νββ$) of $^{136}$Xe in a tonne-scale liquid xenon time projection chamber (TPC). The nEXO TPC will be equipped with charge collection tiles to form the anode. In this work, the charge reconstruction performance of this anode design is studied with a dedicated simulation package. A multi-variate method and a deep neural network are developed to distinguish simulated $0νββ$ signals from backgrounds arising from trace levels of natural radioactivity in the detector materials. These simulations indicate that the nEXO TPC with charge-collection tiles shows promising capability to discriminate the $0νββ$ signal from backgrounds. The estimated half-life sensitivity for $0νββ$ decay is improved by $\sim$20$~(32)\%$ with the multi-variate~(deep neural network) methods considered here, relative to the sensitivity estimated in the nEXO pre-conceptual design report.