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

Abstract

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.

Authors:
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Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics; USDOE Office of Science (SC), Nuclear Physics (NP)
Contributing Org.:
DarkSide-50 Collaboration
OSTI Identifier:
1971260
Alternate Identifier(s):
OSTI ID: 1958474
Report Number(s):
FERMILAB-PUB-23-043-AD-CSAID-ND; arXiv:2302.01830
Journal ID: ISSN 1434-6052; 322; PII: 11410
Grant/Contract Number:  
DEAC02-07CH11359; DEAC05-76RL01830; DEFG02-91ER40671; 20-152; AC02-07CH11359; FG02-91ER40671; AC05-76RL01830
Resource Type:
Published Article
Journal Name:
European Physical Journal. C, Particles and Fields (Online)
Additional Journal Information:
Journal Name: European Physical Journal. C, Particles and Fields (Online) Journal Volume: 83 Journal Issue: 4; Journal ID: ISSN 1434-6052
Publisher:
Springer Science + Business Media
Country of Publication:
Germany
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

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., and DarkSide-50 Collaboration. Search for low mass dark matter in DarkSide-50: the bayesian network approach. Germany: N. p., 2023. Web. doi:10.1140/epjc/s10052-023-11410-4.
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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., & DarkSide-50 Collaboration. Search for low mass dark matter in DarkSide-50: the bayesian network approach. Germany. https://doi.org/10.1140/epjc/s10052-023-11410-4
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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., and DarkSide-50 Collaboration. Mon . "Search for low mass dark matter in DarkSide-50: the bayesian network approach". Germany. https://doi.org/10.1140/epjc/s10052-023-11410-4.
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@article{osti_1971260,
title = {Search for low mass dark matter in DarkSide-50: the bayesian network approach},
author = {Agnes, P. and Albuquerque, I. F. M. and Alexander, T. and Alton, A. K. and Ave, M. and Back, H. O. and Batignani, G. and Biery, K. and Bocci, V. and Bonivento, W. M. and Bottino, B. and Bussino, S. and Cadeddu, M. and Cadoni, M. and Calaprice, F. and Caminata, A. and Campos, M. D. and Canci, N. and Caravati, M. and Cargioli, N. and Cariello, M. and Carlini, M. and Cataudella, V. and Cavalcante, P. and Cavuoti, S. and Chashin, S. and Chepurnov, A. and Cicalò, C. and Covone, G. and D’Angelo, D. and Davini, S. and De Candia, A. and De Cecco, S. and De Filippis, G. and De Rosa, G. and Derbin, A. V. and Devoto, A. and D’Incecco, M. and Dionisi, C. and Dordei, F. and Downing, M. and D’Urso, D. and Fairbairn, M. and Fiorillo, G. and Franco, D. and Gabriele, F. and Galbiati, C. and Ghiano, C. and Giganti, C. and Giovanetti, G. K. and Goretti, A. M. and Grilli di Cortona, G. and Grobov, A. and Gromov, M. and Guan, M. and Gulino, M. and Hackett, B. R. and Herner, K. and Hessel, T. and Hosseini, B. and Hubaut, F. and Hungerford, E. V. and Ianni, An. and Ippolito, V. and Keeter, K. and Kendziora, C. L. and Kimura, M. and Kochanek, I. and Korablev, D. and Korga, G. and Kubankin, A. and Kuss, M. and La Commara, M. and Lai, M. and Li, X. and Lissia, M. and Longo, G. and Lychagina, O. and Machulin, I. N. and Mapelli, L. P. and Mari, S. M. and Maricic, J. and Messina, A. and Milincic, R. and Monroe, J. and Morrocchi, M. and Mougeot, X. and Muratova, V. N. and Musico, P. and Nozdrina, A. O. and Oleinik, A. and Ortica, F. and Pagani, L. and Pallavicini, M. and Pandola, L. and Pantic, E. and Paoloni, E. and Pelczar, K. and Pelliccia, N. and Piacentini, S. and Pocar, A. and Poehlmann, D. M. and Pordes, S. and Poudel, S. S. and Pralavorio, P. and Price, D. D. and Ragusa, F. and Razeti, M. and Razeto, A. and Renshaw, A. L. and Rescigno, M. and Rode, J. and Romani, A. and Sablone, D. and Samoylov, O. and Sandford, E. and Sands, W. and Sanfilippo, S. and Savarese, C. and Schlitzer, B. and Semenov, D. A. and Shchagin, A. and Sheshukov, A. and Skorokhvatov, M. D. and Smirnov, O. and Sotnikov, A. and Stracka, S. and Suvorov, Y. and Tartaglia, R. and Testera, G. and Tonazzo, A. and Unzhakov, E. V. and Vishneva, A. and Vogelaar, R. B. and Wada, M. and Wang, H. and Wang, Y. and Westerdale, S. and Wojcik, M. M. and Xiao, X. and Yang, C. and Zuzel, G. and DarkSide-50 Collaboration},
abstractNote = {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.},
doi = {10.1140/epjc/s10052-023-11410-4},
journal = {European Physical Journal. C, Particles and Fields (Online)},
number = 4,
volume = 83,
place = {Germany},
year = {Mon Apr 24 00:00:00 EDT 2023},
month = {Mon Apr 24 00:00:00 EDT 2023}
}
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