https://doi.org/10.1140/epjc/s10052-023-11410-4
Regular Article - Experimental Physics
Search for low mass dark matter in DarkSide-50: the bayesian network approach
1
Department of Physics, Royal Holloway University of London, TW20 0EX, Egham, UK
2
Instituto de Física, Universidade de São Paulo, 05508-090, São Paulo, Brazil
3
Pacific Northwest National Laboratory, 99352, Richland, WA, USA
4
Physics Department, Augustana University, 57197, Sioux Falls, SD, USA
5
INFN Pisa, 56127, Pisa, Italy
6
Physics Department, Università degli Studi di Pisa, 56127, Pisa, Italy
7
Fermi National Accelerator Laboratory, 60510, Batavia, IL, USA
8
INFN Sezione di Roma, 00185, Rome, Italy
9
INFN Cagliari, 09042, Cagliari, Italy
10
Physics Department, Università degli Studi di Genova, 16146, Genoa, Italy
11
INFN Genova, 16146, Genoa, Italy
12
INFN Roma Tre, 00146, Rome, Italy
13
Mathematics and Physics Department, Università degli Studi Roma Tre, 00146, Rome, Italy
14
Physics Department, Università degli Studi di Cagliari, 09042, Cagliari, Italy
15
Physics Department, Princeton University, 08544, Princeton, NJ, USA
16
Physics, Kings College London, Strand, WC2R 2LS, London, UK
17
INFN Laboratori Nazionali del Gran Sasso, 67100, Assergi, AQ, Italy
18
Gran Sasso Science Institute, 67100, L’Aquila, Italy
19
Chemistry and Pharmacy Department, Università degli Studi di Sassari, 07100, Sassari, Italy
20
INFN Laboratori Nazionali del Sud, 95123, Catania, Italy
21
Physics Department, Università degli Studi “Federico II” di Napoli, 80126, Naples, Italy
22
INFN Napoli, 80126, Naples, Italy
23
Virginia Tech, 24061, Blacksburg, VA, USA
24
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119234, Moscow, Russia
25
Physics Department, Università degli Studi di Milano, 20133, Milan, Italy
26
INFN Milano, 20133, Milan, Italy
27
Physics Department, Sapienza Università di Roma, 00185, Rome, Italy
28
Saint Petersburg Nuclear Physics Institute, 188350, Gatchina, Russia
29
Amherst Center for Fundamental Interactions and Physics Department, University of Massachusetts, 01003, Amherst, MA, USA
30
APC, Université de Paris, CNRS, Astroparticule et Cosmologie, 75013, Paris, France
31
LPNHE, CNRS/IN2P3, Sorbonne Université, Université Paris Diderot, 75252, Paris, France
32
INFN Laboratori Nazionali di Frascati, 00044, Frascati, Italy
33
National Research Centre Kurchatov Institute, 123182, Moscow, Russia
34
National Research Nuclear University MEPhI, 115409, Moscow, Russia
35
Joint Institute for Nuclear Research, 141980, Dubna, Russia
36
Institute of High Energy Physics, 100049, Beijing, China
37
Engineering and Architecture Faculty, Università di Enna Kore, 94100, Enna, Italy
38
Centre de Physique des Particules de Marseille, Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
39
Department of Physics, University of Houston, 77204, Houston, TX, USA
40
School of Natural Sciences, Black Hills State University, 57799, Spearfish, SD, USA
41
AstroCeNT, Nicolaus Copernicus Astronomical Center, 00-614, Warsaw, Poland
42
Radiation Physics Laboratory, Belgorod National Research University, 308007, Belgorod, Russia
43
Physics and Astronomy Department, University of California, 90095, Los Angeles, CA, USA
44
Department of Physics and Astronomy, University of Hawai’i, 96822, Honolulu, HI, USA
45
Université Paris-Saclay, CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), 91120, Palaiseau, France
46
Chemistry, Biology and Biotechnology Department, Università degli Studi di Perugia, 06123, Perugia, Italy
47
INFN Perugia, 06123, Perugia, Italy
48
Department of Physics, University of California, 95616, Davis, CA, USA
49
M. Smoluchowski Institute of Physics, Jagiellonian University, 30-348, Krakow, Poland
50
The University of Manchester, M13 9PL, Manchester, UK
51
Department of Physics and Astronomy, University of California, 92507, Riverside, CA, USA
dh
stefano.piacentini@uniroma1.it
Received:
6
February
2023
Accepted:
17
March
2023
Published online:
24
April
2023
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.
© The Author(s) 2023
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Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.
