The analysis of individual X-ray sources that appear in a crowded field can easily be compromised by the misallocation of recorded events to their originating sources. Even with a small number of sources, that nonetheless have overlapping point spread functions, the allocation of events to sources is a complex task that is subject to uncertainty. We develop a Bayesian method designed to sift high-energy photon events from multiple sources with overlapping point spread functions, leveraging the differences in their spatial, spectral, and temporal signatures. The method probabilistically assigns each event to a given source. Such a disentanglement allows more detailed spectral or temporal analysis to focus on the individual component in isolation, free of contamination from other sources or the background. We are also able to compute source parameters of interest like their locations, relative brightness, and background contamination, while accounting for the uncertainty in event assignments. Simulation studies that include event arrival time information demonstrate that the temporal component improves event disambiguation beyond using only spatial and spectral information. The proposed methods correctly allocate up to 65% more events than the corresponding algorithms that ignore event arrival time information. We apply our methods to two stellar X-ray binaries, UV Cet and HBC515 A, observed with Chandra. We demonstrate that our methods are capable of removing the contamination due to a strong flare on UV Cet B in its companion approximately 40 times weaker during that event, and that evidence for spectral variability at timescales of a few ks can be determined in HBC515 Aa and HBC515 Ab.
翻译:在一个拥挤的字段中出现的单个X光源的分析很容易被记录的事件与其源源的错误分布而受到影响。即使有少量的来源,尽管有重叠的点分布功能,但将事件分配给源是一个复杂的任务。我们开发了一种巴伊西亚方法,从多个来源筛选高能光子事件,其点分布功能相互重叠,利用事件到达时间信息的差异。这种方法概率性地将每个事件指定给某个源。这种分解使得光谱或时间分析能够更加详细地侧重于单个组成部分,而不受其他来源或背景的污染。我们还能够将利益源参数如其位置、相对光亮度和背景污染等纳入不确定因素。我们开发了一种巴伊西亚方法,从多个来源中筛选高能光子事件,同时计算出其空间分布功能、光谱和时间特征的差别。该方法可以使事件变模糊性超出仅使用空间和光谱信息的范围。这种方法可以正确分配到比相应算法多65%的事件,从而忽略事件到达的时间、不受其他来源或背景的污染。我们也可以将源源源参数推算出源值的源源值参数参数参数参数参数,例如:15,我们在C-CRAbrahl 和BC的BC的BC的精确的精确的精确的频率,在两次中可以测量中可以测量中可以测量到一个比。