  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.
