University of Cambridge > > Kavli Institute for Cosmology Seminars > Maximum-Likelihood Biases in PSF and Model-Fitting Photometry

Maximum-Likelihood Biases in PSF and Model-Fitting Photometry

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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: while a 1% bias is expected for a 10 sigma point source, a 10 sigma 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, Hyper Suprime-Cam fake object pipeline (SynPipe), and observations from Sloan Digital Sky Survey Stripe 82.

This talk is part of the Kavli Institute for Cosmology Seminars series.

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