Поиск :
Личный кабинет :
Электронный каталог: Agnes, P. - Search for Low Mass Dark Matter in DarkSide-50: the Bayesian Network Approach
Agnes, P. - Search for Low Mass Dark Matter in DarkSide-50: the Bayesian Network Approach
Статья
Автор: Agnes, P.
The European Physical Journal C [Electronic resource]: Search for Low Mass Dark Matter in DarkSide-50: the Bayesian Network Approach
б.г.
ISBN отсутствует
Автор: Agnes, P.
The European Physical Journal C [Electronic resource]: Search for Low Mass Dark Matter in DarkSide-50: the Bayesian Network Approach
б.г.
ISBN отсутствует
На полку
Статья
Agnes, P.
Search for Low Mass Dark Matter in DarkSide-50: the Bayesian Network Approach / P.Agnes, M.Gromov, D.Korablev, O.Lychagina, O.Samoylov, A.Sheshukov, O.Smirnov, A.Sotnikov, A.Vishneva, [a.o.] // The European Physical Journal C [Electronic resource]. – 2023. – Vol.83, No.4. – P.322. – URL: https://doi.org/10.1140/epjc/s10052-023-11410-4. – Bibliogr.:78.
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.
ОИЯИ = ОИЯИ (JINR)2023
Спец.(статьи,препринты) = С 322.2 - Космология. Гравитационные волны
Спец.(статьи,препринты) = С 17 б - Численное интегрирование. Методы Монте-Карло
Бюллетени = 41/023
Agnes, P.
Search for Low Mass Dark Matter in DarkSide-50: the Bayesian Network Approach / P.Agnes, M.Gromov, D.Korablev, O.Lychagina, O.Samoylov, A.Sheshukov, O.Smirnov, A.Sotnikov, A.Vishneva, [a.o.] // The European Physical Journal C [Electronic resource]. – 2023. – Vol.83, No.4. – P.322. – URL: https://doi.org/10.1140/epjc/s10052-023-11410-4. – Bibliogr.:78.
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.
ОИЯИ = ОИЯИ (JINR)2023
Спец.(статьи,препринты) = С 322.2 - Космология. Гравитационные волны
Спец.(статьи,препринты) = С 17 б - Численное интегрирование. Методы Монте-Карло
Бюллетени = 41/023
Привязано к:
Нет экз.