On the need for synthetic data and robust data simulators in the 2020s
Molly S. Peeples,
Bjorn Emonts,
Mark Kyprianou
et al.
Abstract:As observational datasets become larger and more complex, so too are the questions being asked of these data. Data simulations, i.e., synthetic data with properties (pixelization, noise, PSF, artifacts, etc.) akin to real data, are therefore increasingly required for several purposes, including:(1) testing complicated measurement methods, (2) comparing models and astrophysical simulations to observations in a manner that requires as few assumptions about the data as possible, (3) predicting observational resul… Show more
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