利用图和先验知识对混合场图像进行去混淆
De-Confusing blended field images using graphs and bayesian priors
摘要：
我们提出了一种新的技术来克服混淆噪声在深远红外\Herschel空间望远镜图像利用较短的$λ<2$ \micron波长的先验信息。对于由\Herschels获得的最深的图像，由于源混淆的通量极限比由于仪器噪声和（平滑）天空背景的通量极限亮约三倍。我们研究了通过使用强贝叶斯先验的位置和弱先验的流量的源去混淆模拟\Herschel PACS-160 \micron图像的可能性。我们找到混合源，并将它们分组在一起，同时拟合它们的通量。我们推导出后验概率分布函数的流量服从这些先验通过蒙特卡罗马尔可夫链（MCMC）抽样拟合的图像。假设我们可以预测FIR通量的源的紫外光部分的SED的数量级内，模拟表明，我们可以获得可靠的通量和不确定性的至少三个因素比混淆噪声限制$3σ_{c} $ =2.7 mJy在我们模拟的PACS-160图像。这种技术原则上可以用来减轻源混淆的影响，在任何情况下，一个有先验信息的位置和合理的通量的混合源。对于\Herschel，应用这种技术将提高我们在高红移正常星系中约束尘埃含量的能力。
英文摘要：
We present a new technique for overcoming confusion noise in deep far-infrared \Herschel space telescope images making use of prior information from shorter $λ<2$\micron wavelengths. For the deepest images obtained by \Herschels, the flux limit due to source confusion is about a factor of three brighter than the flux limit due to instrumental noise and (smooth) sky background. We have investigated the possibility of de-confusing simulated \Herschel PACS-160\micron images by using strong Bayesian priors on the positions and weak priors on the flux of sources. We find the blended sources and group them together and simultaneously fit their fluxes. We derive the posterior probability distribution function of fluxes subject to these priors through Monte Carlo Markov Chain (MCMC) sampling by fitting the image. Assuming we can predict FIR flux of sources based on ultraviolet-optical part of their SEDs to within an order of magnitude, the simulations show that we can obtain reliable fluxes and uncertainties at least a factor of three fainter than the confusion noise limit of $3σ_{c} $=2.7 mJy in our simulated PACS-160 image. This technique could in principle be used to mitigate the effects of source confusion in any situation where one has prior information of positions and plausible fluxes of blended sources. For \Herschel, application of this technique will improve our ability to constrain the dust content in normal galaxies at high redshift.
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