论文标题

Bernoulli概括性比测试用于从光子计数图像的信号检测

Bernoulli generalized likelihood ratio test for signal detection from photon counting images

论文作者

Hu, Mengya, Sun, He, Harness, Anthony, Kasdin, N. Jeremy

论文摘要

由于系外行星非常昏暗,因此需要在光子计数(PC)模式下运行的电子乘以带电的耦合器件(EMCCD)才能降低检测器噪声水平并实现其检测。通常,在处理之前,将PC图像作为共添加图像添加在一起。我们在这里提出了一种信号检测和估计技术,该技术直接与单个PC图像一起使用。该方法基于广义的似然比测试(GLRT),并在PC图像之间使用Bernoulli分布。 Bernoulli分布来自检测器的随机模型,该模型准确地代表了其噪声特征。我们表明,我们的技术优于先前使用的GLRT方法,该方法依赖于高斯噪声假设下的共添加图像和基于信噪比(SNR)的两个检测算法。此外,我们的方法在进行检测时提供了外部强度和背景强度的最大似然估计。它可以在线应用,因此一旦达到了指定的阈值,就可以停止观察,从而为行星的存在(或不存在)提供信心。结果,观察时间有效地使用了。除了观察时间外,本文中引入的检测性能的分析还提供了成像参数选择(例如阈值)的定量指导。最后,尽管这项工作集中在检测点源的示例上,但该框架广泛适用。

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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