自适应测量精化中信噪比参数结构推断的贝叶斯方法
A Bayesian approach to the inference of parametric configuration of the signal-to-noise ratio in an adaptive refinement of the measurements
摘要：
校准是当今从测量中提取有价值数据的最重要过程之一。目前能否从一套不同的仪器和调查中获得最佳数据立方体，取决于在相应的计量分析中采用系统和稳健的方法。在这个意义上，可配置仪器参数的推断可以显著提高所获得的数据的质量。本文提出了一种基于贝叶斯推断的解决方案，用于估计与信噪比相关的可配置参数。通过解决这个问题所获得的信息可以以非常有用的方式处理，如果它被认为是整体测量策略的自适应循环的一部分，以这种方式，该参数推断的结果导致在测量解释的上下文中的模型比较问题的知识增加。这个问题的背景是多波长测量来自不同的宇宙学调查和获得各种望远镜仪器。作为第一步，将根据现代望远镜仪器的当前最新技术水平对典型噪声贡献进行彻底分析，然后第二步将包括确定与所考虑的噪声模型相关的可配置参数，用于所选择的一般测量环境。然后，作为第三步，将应用用于这些参数估计的贝叶斯推断，同时考虑到滋扰参数的适当识别和先验概率的适当选择。最后，将从这里提出的方法的实施结果中得出一组相应的结论
英文摘要：
Calibration is nowadays one of the most important processes involved in the extraction of valuable data from measurements. The current availability of an optimum data cube measured from a heterogeneous set of instruments and surveys relies on a systematic and robust approach in the corresponding measurement analysis. In that sense, the inference of configurable instrument parameters can considerably increase the quality of the data obtained. This paper proposes a solution based on Bayesian inference for the estimation of the configurable parameters relevant to the signal to noise ratio. The information obtained by the resolution of this problem can be handled in a very useful way if it is considered as part of an adaptive loop for the overall measurement strategy, in such a way that the outcome of this parametric inference leads to an increase in the knowledge of a model comparison problem in the context of the measurement interpretation. The context of this problem is the multi-wavelength measurements coming from diverse cosmological surveys and obtained with various telescope instruments. As a first step,, a thorough analysis of the typical noise contributions will be performed based on the current state-of-the-art of modern telescope instruments, a second step will then consist of identifying configurable parameters relevant to the noise model under consideration, for a generic context of measurement chosen. Then as a third step a Bayesian inference for these parameters estimation will be applied, taking into account a proper identification of the nuisance parameters and the adequate selection of a prior probability. Finally, a corresponding set of conclusions from the results of the implementation of the method proposed here will be derived
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