论文标题
最佳的统计推断在存在系统不确定性的情况下使用基于binned泊松可能性的神经网络优化具有滋扰参数
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
论文作者
论文摘要
如果观察结果和观察值跨越了高维输入空间，那么科学中的数据分析，例如高能粒子物理学通常会受到棘手的可能性。通常，通过使用特征工程和直方图降低维数来解决该问题，从而使后一种技术允许使用泊松统计量建立可能性。然而，在可能存在滋扰参数代表的系统不确定性的情况下，最佳维度降低而最小而言，最小的信息丢失了有关感兴趣参数的信息。这项工作提出了一种新的策略，可以通过神经网络进行功能工程和直方图的差异表述来构建维度降低，从而可以通过统计推断的结果来优化完整的工作流程，例如，感兴趣的参数的方差，作为目标。我们讨论了这种方法如何导致对感兴趣的参数的估计，该参数接近最佳，并以基于伪证明的简单示例和高能粒子物理学的更复杂的示例来证明该技术的适用性。
Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the dimensionality using feature engineering and histograms, whereby the latter technique allows to build the likelihood using Poisson statistics. However, in the presence of systematic uncertainties represented by nuisance parameters in the likelihood, the optimal dimensionality reduction with a minimal loss of information about the parameters of interest is not known. This work presents a novel strategy to construct the dimensionality reduction with neural networks for feature engineering and a differential formulation of histograms so that the full workflow can be optimized with the result of the statistical inference, e.g., the variance of a parameter of interest, as objective. We discuss how this approach results in an estimate of the parameters of interest that is close to optimal and the applicability of the technique is demonstrated with a simple example based on pseudo-experiments and a more complex example from high-energy particle physics.
