Day 1744

Tuesday.

2008.03446

Using Gaia DR2 to solve differential color refraction and charge transfer efficiency issues

Lin, et al

The Gaia DR2 catalog released in 2018 gives information about more than one billion stars, including their extremely precise positions that are not affected by the atmosphere, as well as the magnitudes in the G, RP, and BP passbands. This information provides great potential for the improvement of the ground-based astrometry. Based on Gaia DR2, we present a convenient method to calibrate the differential color refraction (DCR). This method only requires observations with dozens of stars taken through a selected filter. Applying this method to the reduction of observations captured through different filters by the 1-m and 2.4-m telescopes at Yunnan Observatory, the results show that the mean of the residuals between observed and computed positions (O-C) after DCR correction is significantly reduced. For our observations taken through an N (null) filter, the median of the mean (O-C) for well-exposed stars (about 15 G-mag) decreases from 19 mas to 3 mas, thus achieving better accuracy, i.e. mean (O-C). Another issue correlated is a systematic error caused by the poor charge transfer efficiency (CTE) when a CCD frame is read out. This systematic error is significant for some of the observations captured by the 1-m telescope at Yunnan Observatory. Using a sigmoidal function to fit and correct the mean (O-C), a systematic error up to 30 mas can be eliminated.

2008.03833

Deep generative models for galaxy image simulations

Lanusse, Mandelbaum, et al

Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available space-based data. In this work, we propose a methodology based on Deep Generative Models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and PSF-convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the Point Spread Function and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second- and higher-order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce GalSim-Hub, a community-driven repository of generative models, and a framework for incorporating generative models within the GalSim image simulation software.