泊松噪声中信源的极大似然检测
Maximum-likelihood detection of sources among Poissonian noise
摘要：
研究了一种用于检测X射线天空图像中致密源的极大似然（ML）技术。这样的图像，在相对较低的曝光制度，目前的X射线天文台，表现出泊松噪声在背景通量水平。各种源检测方法进行了比较，通过蒙特卡罗，ML检测方法被证明是比较有利的优化线性滤波器（OLF）的方法时，适用于一个单一的图像。在不同的能量带的几个图像上并行进行检测，ML方法被证明具有一些实际的优势，这使得它优于OLF方法。ML的一些批评进行了讨论。最后给出了一种实用的ML探测灵敏度估计方法，该方法同样适用于滑盒源探测。
英文摘要：
A maximum likelihood (ML) technique for detecting compact sources in images of the x-ray sky is examined. Such images, in the relatively low exposure regime accessible to present x-ray observatories, exhibit Poissonian noise at background flux levels. A variety of source detection methods are compared via Monte Carlo, and the ML detection method is shown to compare favourably with the optimized-linear-filter (OLF) method when applied to a single image. Where detection proceeds in parallel on several images made in different energy bands, the ML method is shown to have some practical advantages which make it superior to the OLF method. Some criticisms of ML are discussed. Finally, a practical method of estimating the sensitivity of ML detection is presented, and is shown to be also applicable to sliding-box source detection.
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