2019/02/28 by Stephen K. N. Portillo, Joshua S. Speagle, Douglas P. Finkbeiner
Physics and Astronomy · #astro-ph.IM
paper · pdf · doi:10.3847/1538-3881/ab76ba
published as AJ 159 165 (2020) · 35 pages, 13 figures, accepted to AJ; code and data available online at https://github.com/joshspeagle/phot_bias
arxiv created 2020/02/14 · arxiv updated 2020/03/25
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 10σ point source, a 10σ 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. forced photometry) gives flux estimates in those bands that are biased low, compounding a bias in derived colors. We show that these effects are present in idealized simulations, outputs from the Hyper Suprime-Cam fake object pipeline (SynPipe), and observations from Sloan Digital Sky Survey Stripe 82. Prescriptions to correct for the ML bias in flux, and its uncertainty, are provided.