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Journal Articles European Physical Journal C: Particles and Fields Year : 2023

Search for low mass dark matter in DarkSide-50: the bayesian network approach

P. Agnes (1) , I.F.M. Albuquerque (2) , T. Alexander (3) , A.K. Alton (4) , M. Ave (2) , H.O. Back (3) , G. Batignani (5) , K. Biery (6) , V. Bocci (7) , W.M. Bonivento (8) , B. Bottino (9) , S. Bussino (7) , M. Cadeddu (8) , M. Cadoni (8) , F. Calaprice (10) , A. Caminata (11) , M. D. Campos (12) , N. Canci (13) , M. Caravati (8) , N. Cargioli (8) , M. Cariello (11) , M. Carlini (13) , V. Cataudella (14) , P. Cavalcante (13) , S. Cavuoti (14) , S. Chashin (15) , A. Chepurnov (15) , C. Cicalò (8) , G. Covone (14) , D. D’angelo (16) , S. Davini (11) , A. De Candia (14) , S. De Cecco (7) , G. De Filippis (14) , G. De Rosa (14) , A. V. Derbin (17) , A. Devoto (8) , M. D’incecco (18) , C. Dionisi (19) , F. Dordei (8) , M. Downing (20) , D. D’urso (21) , M. Fairbairn (12) , G. Fiorillo (14) , D. Franco (22) , F. Gabriele (8) , C. Galbiati (10) , C. Ghiano (13) , C. Giganti (23) , G. K. Giovanetti (10) , A. M. Goretti (13) , G. Grilli Di Cortona (7) , A. Grobov (24) , M. Gromov (25) , M. Guan (26) , M. Gulino (27) , B. R. Hackett (3) , K. Herner (6) , T. Hessel (22) , B. Hosseini (8) , F. Hubaut (28) , E. V. Hungerford (29) , An. Ianni (10) , V. Ippolito (7) , K. Keeter (30) , C. L. Kendziora (6) , M. Kimura (31) , I. Kochanek (13) , D. Korablev (32) , G. Korga (13) , A. Kubankin (33) , M. Kuss (5) , M. La Commara (14) , M. Lai (8) , X. Li (10) , M. Lissia (8) , G. Longo (14) , O. Lychagina (15) , I. N. Machulin (34) , L. P. Mapelli (35) , S. M. Mari (7) , J. Maricic (36) , A. Messina (7) , R. Milincic (36) , J. Monroe (1) , M. Morrocchi (5) , X. Mougeot (37) , V. N. Muratova (17) , P. Musico (11) , A. O. Nozdrina (24) , A. Oleinik (33) , F. Ortica (38) , L. Pagani (39) , M. Pallavicini (11) , L. Pandola (40) , E. Pantic (39) , E. Paoloni (5) , K. Pelczar (13) , N. Pelliccia (41) , S. Piacentini (7) , A. Pocar (20) , D. M. Poehlmann (39) , S. Pordes (6) , S. S. Poudel (29) , P. Pralavorio (28) , D. D. Price (42) , F. Ragusa (43) , M. Razeti (8) , A. Razeto (13) , A. L. Renshaw (29) , M. Rescigno (7) , J. Rode (22, 23) , A. Romani (38) , D. Sablone (10) , O. Samoylov (32) , E. Sandford (42) , W. Sands (10) , S. Sanfilippo (40) , C. Savarese (10) , B. Schlitzer (39) , D. A. Semenov (17) , A. Shchagin (33) , A. Sheshukov (32) , M. D. Skorokhvatov (34, 25) , O. Smirnov (32) , A. Sotnikov (32) , S. Stracka (5) , Y. Suvorov (14) , R. Tartaglia (13) , G. Testera (11) , A. Tonazzo (22) , E. V. Unzhakov (17) , A. Vishneva (32) , R. B. Vogelaar (44) , M. Wada (45, 31) , H. Wang (35) , Y. Wang (35, 26) , S. Westerdale (46) , M. M. Wojcik (41) , X. Xiao (35) , C. Yang (26) , G. Zuzel (41)
1 RHUL - Royal Holloway [University of London]
2 Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]
3 PNNL - Pacific Northwest National Laboratory
4 Augustana University
5 INFN - Istituto Nazionale di Fisica Nucleare [Pisa]
6 Fermilab - Fermi National Accelerator Laboratory
7 INFN - Istituto Nazionale di Fisica Nucleare [Sezione di Roma 1]
8 INFN, Sezione di Cagliari - Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari
9 UniGe - Università degli studi di Genova = University of Genoa
10 Princeton University
11 INFN, Sezione di Genova - Istituto Nazionale di Fisica Nucleare, Sezione di Genova
12 King‘s College London
13 INFN - INFN Laboratori Nazionali del Gran Sasso,
14 University of Naples Federico II = Università degli studi di Napoli Federico II
15 SINP - D.V. Skobeltsyn Institute of Nuclear Physics
16 UNIMI - Università degli Studi di Milano = University of Milan
17 Saint Petersburg Nuclear Physics Institute RAS
18 INFN Laboratori Nazionali del Gran Sasso
19 INFN, Sezione di Roma 3 - Istituto Nazionale di Fisica Nucleare, Sezione di Roma 3
20 UMass Amherst - University of Massachusetts [Amherst]
21 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
22 APC (UMR_7164) - AstroParticule et Cosmologie
23 LPNHE (UMR_7585) - Laboratoire de Physique Nucléaire et de Hautes Énergies
24 NRC KI - National Research Center "Kurchatov Institute"
25 MEPhI - Moscow State Engineering Physics Institute
26 IHEP - Institute of High Energy Physics [Beijing]
27 INFN---Laboratori Nazionali del Sud (LNS), Via S. Sofia 62, 95123 Catania, Italy
28 CPPM - Centre de Physique des Particules de Marseille
29 University of Houston
30 Black Hills State University
31 AstroCeNT - Nicolaus Copernicus Astronomical Cente
32 JINR - Joint Institute for Nuclear Research
33 Belgorod National Research University, 308015 Belgorod, Russia
34 Kurchatov NBIC Centre, National Research Centre, Kurchatov Institute
35 UC - University of California
36 UH - University of Hawai'i [Honolulu]
37 LNHB - Laboratoire National Henri Becquerel
38 UNIPG - Università degli Studi di Perugia = University of Perugia
39 UC Davis - University of California [Davis]
40 INFN - Laboratori Nazionali del Sud
41 UJ - Uniwersytet Jagielloński w Krakowie = Jagiellonian University
42 University of Manchester [Manchester]
43 INFN - Istituto Nazionale di Fisica Nucleare, Sezione di Milano
44 Virginia Tech [Blacksburg]
45 UniCa - Università degli Studi di Cagliari = University of Cagliari
46 UC Riverside - University of California [Riverside]
P. Agnes
A.K. Alton
  • Function : Author
C. Galbiati
  • Function : Author
An. Ianni
  • Function : Author
X. Li
  • Function : Author
D. Sablone
  • Function : Author
W. Sands
  • Function : Author
C. Savarese
  • Function : Author
H. Wang
  • Function : Author
X. Xiao
  • Function : Author

Abstract

We 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.
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Dates and versions

cea-04161899 , version 1 (13-07-2023)

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P. Agnes, I.F.M. Albuquerque, T. Alexander, A.K. Alton, M. Ave, et al.. Search for low mass dark matter in DarkSide-50: the bayesian network approach. European Physical Journal C: Particles and Fields, 2023, 83, pp.322. ⟨10.1140/epjc/s10052-023-11410-4⟩. ⟨cea-04161899⟩
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