Author:
Agnes P.,Albuquerque I. F. M.,Alexander T.,Alton A. K.,Ave M.,Back H. O.,Batignani G.,Biery K.,Bocci V.,Bonivento W. M.,Bottino B.,Bussino S.,Cadeddu M.,Cadoni M.,Calaprice F.,Caminata A.,Campos M. D.,Canci N.,Caravati M.,Cargioli N.,Cariello M.,Carlini M.,Cataudella V.,Cavalcante P.,Cavuoti S.,Chashin S.,Chepurnov A.,Cicalò C.,Covone G.,D’Angelo D.,Davini S.,De Candia A.,De Cecco S.,De Filippis G.,De Rosa G.,Derbin A. V.,Devoto A.,D’Incecco M.,Dionisi C.,Dordei F.,Downing M.,D’Urso D.,Fairbairn M.,Fiorillo G.,Franco D.,Gabriele F.,Galbiati C.,Ghiano C.,Giganti C.,Giovanetti G. K.,Goretti A. M.,Grilli di Cortona G.,Grobov A.,Gromov M.,Guan M.,Gulino M.,Hackett B. R.,Herner K.,Hessel T.,Hosseini B.,Hubaut F.,Hungerford E. V.,Ianni An.,Ippolito V.,Keeter K.,Kendziora C. L.,Kimura M.,Kochanek I.,Korablev D.,Korga G.,Kubankin A.,Kuss M.,La Commara M.,Lai M.,Li X.,Lissia M.,Longo G.,Lychagina O.,Machulin I. N.,Mapelli L. P.,Mari S. M.,Maricic J.,Messina A.,Milincic R.,Monroe J.,Morrocchi M.,Mougeot X.,Muratova V. N.,Musico P.,Nozdrina A. O.,Oleinik A.,Ortica F.,Pagani L.,Pallavicini M.,Pandola L.,Pantic E.,Paoloni E.,Pelczar K.,Pelliccia N.,Piacentini S.,Pocar A.,Poehlmann D. M.,Pordes S.,Poudel S. S.,Pralavorio P.,Price D. D.,Ragusa F.,Razeti M.,Razeto A.,Renshaw A. L.,Rescigno M.,Rode J.,Romani A.,Sablone D.,Samoylov O.,Sandford E.,Sands W.,Sanfilippo S.,Savarese C.,Schlitzer B.,Semenov D. A.,Shchagin A.,Sheshukov A.,Skorokhvatov M. D.,Smirnov O.,Sotnikov A.,Stracka S.,Suvorov Y.,Tartaglia R.,Testera G.,Tonazzo A.,Unzhakov E. V.,Vishneva A.,Vogelaar R. B.,Wada M.,Wang H.,Wang Y.,Westerdale S.,Wojcik M. M.,Xiao X.,Yang C.,Zuzel G.,
Abstract
AbstractWe present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection with the quantity of interest. No assumptions about the linearity of the problem or the shape of the probability distribution functions are required, and there is no need to morph signal and background spectra as a function of nuisance parameters. By expressing the problem in terms of Bayesian Networks, we have developed an inference algorithm based on a Markov Chain Monte Carlo to calculate the posterior probability. A clever description of the detector response model in terms of parametric matrices allows us to study the impact of systematic variations of any parameter on the final results. Our approach not only provides the desired information on the parameter of interest, but also potential constraints on the response model. Our results are consistent with recent published analyses and further refine the parameters of the detector response model.
Funder
Polish Ministry for Education and Science
Sao Paulo Research Foundation
Science and Technology Facilities Council, United Kingdom
IRAP AstroCeNT funded by FNP from ERDF
UnivEarthS LabEx
Istituto Nazionale di Fisica Nucleare
Interdisciplinary Scientific and Educational School of Moscow University “Fundamental and Applied Space Research”
Polish NCN
Department of Energy
IN2P3-COPIN consortium
National Science Foundation
Institut National de Physique Nucléaire et de Physique des Particules
European Union’s Horizon 2020
Ministry of Education and Science of the Russian Federation for higher education establishments
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