标题： DarkSide-50探测器中寻找低质量暗物质：贝叶斯网络方法 显示英文标题

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

摘要： 我们提出了一种新的方法来搜索DarkSide-50实验中的暗物质，该方法依赖于贝叶斯网络。 此方法将探测器响应模型纳入似然函数中，显式地保持与感兴趣量的联系。 无需假设问题的线性特性或概率分布函数的形状，也无需根据讨厌参数调整信号和背景光谱。 通过用贝叶斯网络表达问题，我们开发了一个基于马尔可夫链蒙特卡洛的推理算法以计算后验概率。 通过对探测器响应模型用参数矩阵进行巧妙描述，我们可以研究任意参数系统变化对最终结果的影响。 我们的方法不仅提供了有关感兴趣参数所需的信息，还对响应模型提供了潜在的约束。 我们的结果与近期发表的分析一致，并进一步细化了探测器响应模型的参数。 显示英文摘要

摘要： 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.