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- W2912990100 abstract "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." @default.
- W2912990100 created "2019-02-21" @default.
- W2912990100 creator A5057507231 @default.
- W2912990100 creator A5063470012 @default.
- W2912990100 creator A5082668142 @default.
- W2912990100 date "2020-03-23" @default.
- W2912990100 modified "2023-10-18" @default.
- W2912990100 title "Photometric Biases in Modern Surveys" @default.
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- W2912990100 doi "https://doi.org/10.3847/1538-3881/ab76ba" @default.
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