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The next generation of telescopes will acquire terabytes of image data on anightly basis. Collectively, these large images will contain billions ofinteresting objects, which astronomers call sources. The astronomers' task isto construct a catalog detailing the coordinates and other properties of thesources. The source catalog is the primary data product for most telescopes andis an important input for testing new astrophysical theories, but to constructthe catalog one must first detect the sources. Existing algorithms for catalogcreation are effective at detecting sources, but do not have rigorousstatistical error control. At the same time, there are several multiple testingprocedures that provide rigorous error control, but they are not designed todetect sources that are aggregated over several pixels. In this paper, wepropose a technique that does both, by providing rigorous statistical errorcontrol on the aggregate objects themselves rather than the pixels. Wedemonstrate the effectiveness of this approach on data from the Chandra X-rayObservatory Satellite. Our technique effectively controls the rate of falsesources, yet still detects almost all of the sources detected by proceduresthat do not have such rigorous error control and have the advantage ofadditional data in the form of follow up observations, which will not beavailable for upcoming large telescopes. In fact, we even detect a new sourcethat was missed by previous studies. The statistical methods developed in thispaper can be extended to problems beyond Astronomy, as we will illustrate withan example from Neuroimaging.

Straight to the Source: Detecting Aggregate Objects in Astronomical Images with Proper Error Control