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Detection noise bias and variance in the power spectrum and bispectrum in optical interferometry

机译:光学干涉术中功率谱和双谱中的检测噪声偏差和方差

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Context. Long-baseline optical interferometry uses the power spectrum and bispectrum constructs as fundamental observables. Noise arising in the detection of the fringe pattern results in both variance and biases in the power spectrum and bispectrum. Previous work on correcting the biases and estimating the variances for these quantities typically includes restrictive assumptions about the sampling of the interferogram and/or about the relative importance of Poisson and Gaussian noise sources. Until now it has been difficult to accurately compensate for systematic biases in data which violates these assumptions. Aims. We seek a formalism to allow the construction of bias-free estimators of the bispectrum and power spectrum, and to estimate their variances, under less restrictive conditions, which include both unevenly-sampled data and measurements affected by a combination of noise sources with Poisson and Gaussian statistics. Methods. We used a method based on the moments of the noise distributions to derive formulae for the biases introduced to the power spectrum and bispectrum when the complex fringe amplitude is derived from an arbitrary linear combination of a set of discrete interferogram measurements. Results. We have derived formulae for bias-free estimators of the power spectrum and bispectrum, which can be used with any linear estimator of the fringe complex amplitude. We have demonstrated the importance of bias-free estimators for the case of the detection of faint companions (for example exoplanets) using closure phase nulling. We have derived formulae for the variance of the power spectrum and have shown how the variance of the bispectrum can be calculated.
机译:上下文。长基线光学干涉测量法将功率谱和双谱构造用作基本可观察物。检测条纹图案时产生的噪声会导致功率谱和双谱的变化和偏差。关于校正这些量的偏差和估计方差的先前工作通常包括关于干涉图采样和/或关于泊松和高斯噪声源的相对重要性的限制性假设。迄今为止,很难准确地补偿违反这些假设的数据中的系统偏差。目的我们寻求一种形式主义,以允许构建双频谱和功率谱的无偏差估计量,并在限制性较小的条件下估计其方差,这些条件包括不均匀采样的数据以及受噪声源与Poisson和高斯统计。方法。当复杂条纹幅度是从一组离散干涉图测量值的任意线性组合中得出时,我们使用了一种基于噪声分布矩的方法来导出引入功率谱和双谱的偏置的公式。结果。我们导出了功率谱和双谱的无偏差估计器的公式,这些公式可与条纹复振幅的任何线性估计器一起使用。我们已经证明了无偏估计器对于使用闭合相位归零法检测微弱伴星(例如系外行星)的重要性。我们导出了功率谱方差的公式,并显示了如何计算双谱方差。

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