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Many surveys use maximum-likelihood (ML) methods to fit models whenextracting photometry from images. We show these ML estimators systematicallyoverestimate the flux as a function of the signal-to-noise ratio and the numberof model parameters involved in the fit. This bias is substantially worse forresolved sources: while a 1% bias is expected for a 10$\sigma$ point source, a10$\sigma$ resolved galaxy with a simplified Gaussian profile suffers a 2.5%bias. This bias also behaves differently depending how multiple bands are usedin the fit: simultaneously fitting all bands leads the flux bias to becomeroughly evenly distributed between them, while fixing the position in"non-detection" bands (i.e. forced photometry) gives flux estimates in thosebands that are biased low, compounding a bias in derived colors. We show thatthese effects are present in idealized simulations, outputs from the HyperSuprime-Cam fake object pipeline (SynPipe), and observations from Sloan DigitalSky Survey Stripe 82. Prescriptions to correct for the ML bias in flux, and itsuncertainty, are provided.

Photometric Biases in Modern Surveys