Photometric Biases in Modern Surveys
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 w...
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| Published in: | The Astronomical journal 2020-04, Vol.159 (4), p.165 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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 10 point source, a 10 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. |
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| ISSN: | 0004-6256 1538-3881 |