Dr. Stephen Portillo, DIRAC Postdoctoral Fellow, coauthored paper “Photometric Biases in Modern Surveys” published in March 2020.

Photometric Biases in Modern Surveys

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.

Dr. Portillo is DIRAC Postdoctoral Fellow and UW Data Science Postdoctoral Fellow in the DIRAC Institute at the University of Washington. In May 2018, Stephen completed his PhD in Astronomy and Astrophysics at Harvard University where he worked under the supervision of Prof. Douglas Finkbeiner on probabilistic cataloguing.