文章基本信息

标题：Optimal Matched Filter in the Low-number Count Poisson Noise Regime and Implications for X-Ray Source Detection
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作者：Eran O. Ofek ; Barak Zackay
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2018
卷号：155
期号：4
页码：1-8
DOI：10.3847/1538-3881/aab265
语种：English
出版社：American Institute of Physics
摘要：Detection of templates (e.g., sources) embedded in low-number count Poisson noise is a common problem inastrophysics. Examples include source detection in X-ray images, γ-rays, UV, neutrinos, and search for clusters of galaxies and stellar streams. However, the solutions in the X-ray-related literature are sub-optimal in some cases by considerable factors. Using the lemma of Neyman–Pearson, we derive the optimal statistics for template detection in the presence of Poisson noise. We demonstrate that, for known template shape (e.g., point sources), this method provides higher completeness, for a fixed false-alarm probability value, compared with filtering the image with the point-spread function (PSF). In turn, we find that filtering by the PSF is better than filtering the image using the Mexican-hat wavelet (used by wavdetect). For some background levels, our method improves the sensitivity of source detection by more than a factor of two over the popular Mexican-hat wavelet filtering. This filtering technique can also be used for fast PSF photometry and flare detection; it is efficient and straightforward to implement. We provide an implementation in MATLAB. The development of a complete code that works on real data, including the complexities of background subtraction and PSF variations, is deferred for future publication.
关键词：methods: statistical;techniques: image processing;techniques: photometric