Estimating galaxy photometry with deep learning

by Laura Cabayol (IFAE)

Wednesday 28 Apr 2021, 13:00 → 14:00 Europe/Madrid

IFAE Virtual Seminar Room (IFAE)

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this talk, we will introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos predicts the background-subtracted flux probability density function. The algorithm is developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts as cosmic rays or scattered light.