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Testing Accuracy and Precision of Existing Photometry Algorithms on Moving Targets

Previous studies determining which astronomical photometry software is best suited for a particular dataset are usually focused on speed, source classification, and/or meeting a sensitivity requirement. For faint objects in particular, the priority is given to maximizing signal-to-noise. Photometry of moving targets offers additional challenges (i) to aperture photometry because background object contamination varies from image to image, and (ii) to routines that build a PSF model from point sources in the image because trailed field stars do not perfectly represent the PSF of the untrailed target. Here, we present the results of testing several photometry algorithms (tphot, DAOPHOT, DoPHOT, APT, and multiple techniques within Source Extractor and IRAF's PHOT) on data for a faint, slow-moving solar system object with a known light curve. We find that the newly-developed tphot software most accurately and precisely reproduces the object's true light curve, with particular advantages in centroiding, exclusion of contaminants from the target's flux, and fitting flux in the wings of the point-spread function.