摘要：Many surveys use maximum-likelihood (ML) methods to fit models when extracting photometry from images.We show that 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.
关键词：Astrostatistics;Astronomy data analysis;Maximum likelihood estimation;Fisher's Information;Astronomy data reduction;Catalogs;Surveys;CCD photometry
