首页> 外文期刊>Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment >Maximum likelihood estimation of spectra information from multiple independent cosmic ray data sets
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Maximum likelihood estimation of spectra information from multiple independent cosmic ray data sets

机译:来自多个独立宇宙射线数据集的光谱信息的最大似然估计

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A procedure based on the method of maximum likelihood (ML) is developed to allow an arbitrary number of cosmic ray data sets produced by different instruments with different energy ranges and resolutions to be used together in the analysis of the spectra. Application of this approach will facilitate the interpretation of energy spectra data from multiple science missions and thereby provide more accurate spectral parameter estimates based on the combination of data sets. The benefits of this technique are measured in terms of the reduction of the statistical errors (standard deviations) and biases of the spectra information using the multiple data sets in concert as compared to the statistical errors of the spectra information when the data sets are considered separately. The Cramer-Rao bound (CRB) is derived for multiple independent data sets and provides the fundamental limit to the precision of spectral parameter determination from the combination of cosmic ray data sets. The CRB is used to quantify the efficiency of the derived procedure and also provides a stopping rule in the search for the best unbiased methodology for quantifying the parameters from the data sets.Application of the ML procedure is demonstrated on two simulated data sets acting singly and then together in the estimation of the three broken power law spectral parameters, along with the numerical details required for its successful implementation in practice.Examples of several detector systematic errors and their impact on the efforts to measure the spectra are also investigated. (C) 2004 Elsevier B.V. All rights reserved.
机译:开发了一种基于最大似然(ML)方法的程序,以允许将由具有不同能量范围和分辨率的不同仪器产生的任意数量的宇宙射线数据集一起用于光谱分析。此方法的应用将有助于解释来自多个科学任务的能谱数据,从而基于数据集的组合提供更准确的谱参数估计。与单独考虑数据集的频谱信息的统计误差相比,使用多个数据集可以减少频谱信息的统计误差(标准偏差)和偏差,从而衡量了该技术的优势。 Cramer-Rao边界(CRB)是针对多个独立数据集得出的,并为结合宇宙射线数据集确定光谱参数的精度提供了基本限制。 CRB用于量化导出过程的效率,还提供了一个停止规则,以寻找最佳的无偏方法来量化数据集中的参数。ML过程在两个单独作用的模拟数据集上的应用得到了证明。然后一起估算了三个破坏的幂律谱参数,以及在实践中成功实施所需的数值细节。还研究了一些探测器系统误差的示例及其对测量谱的努力的影响。 (C)2004 Elsevier B.V.保留所有权利。

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