Photometric Biases in Modern Surveys

Understand

Many surveys use maximum-likelihood (ML) methods to fit models when extracting photometry from images.

• We show these ML estimators systematically overestimate the flux as a function of the signal-to-noise ratio and the number of model parameters involved in the fit.
• This bias is substantially worse for resolved sources: while a 1% bias is expected for a 10$\sigma$ point source, a 10$\sigma$ resolved galaxy with a simplified Gaussian profile suffers a 2.5% bias.
• This bias also behaves differently depending how multiple bands are used in the fit: simultaneously fitting all bands leads the flux bias to become roughly evenly distributed between them, while fixing the position in "non-detection" bands (i.e.