光子计数图像信号检测的Bernoulli广义似然比检验
Bernoulli generalized likelihood ratio test for signal detection from photon counting images
摘要：
由于系外行星非常暗淡，因此需要在光子计数（PC）模式下工作的电子倍增电荷耦合器件（EMCCD）来降低探测器的噪声水平并实现对它们的探测。通常，PC图像在处理之前被添加在一起作为共同添加的图像。我们在这里提出了一个信号检测和估计技术，直接与个人PC图像。该方法是基于广义似然比检验（GLRT），并使用PC图像之间的伯努利分布。伯努利分布是从检测器的随机模型中推导出来的，它准确地表示了它的噪声特性。我们表明，我们的技术优于以前使用的GLRT方法，依赖于共同添加的图像下的高斯噪声假设和两个检测算法的基础上信噪比（SNR）。此外，我们的方法提供了极大似然估计外行星的强度和背景强度，同时进行检测。它可以在线应用，因此一旦达到指定的阈值，就可以停止观测，从而为行星的存在（或不存在）提供信心。结果，有效地利用了观察时间。除了观测时间外，文中还对探测性能进行了分析，对阈值等成像参数的选择给出了定量的指导。最后，虽然本文的工作重点是检测点源的例子，该框架是广泛适用的。
英文摘要：
Because exoplanets are extremely dim, an Electron Multiplying Charged Coupled Device (EMCCD) operating in photon counting (PC) mode is necessary to reduce the detector noise level and enable their detection. Typically, PC images are added together as a co-added image before processing. We present here a signal detection and estimation technique that works directly with individual PC images. The method is based on the generalized likelihood ratio test (GLRT) and uses a Bernoulli distribution between PC images. The Bernoulli distribution is derived from a stochastic model for the detector, which accurately represents its noise characteristics. We show that our technique outperforms a previously used GLRT method that relies on co-added images under a Gaussian noise assumption and two detection algorithms based on signal-to-noise ratio (SNR). Furthermore, our method provides the maximum likelihood estimate of exoplanet intensity and background intensity while doing detection. It can be applied online, so it is possible to stop observations once a specified threshold is reached, providing confidence for the existence (or absence) of planets. As a result, the observation time is efficiently used. Besides the observation time, the analysis of detection performance introduced in the paper also gives quantitative guidance on the choice of imaging parameters, such as the threshold. Lastly, though this work focuses on the example of detecting point source, the framework is widely applicable.
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