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On the need for synthetic data and robust data simulators in the 2020s

Published onSep 30, 2019
On the need for synthetic data and robust data simulators in the 2020s
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Abstract

Synthetic data are increasingly required for several purposes, from testing complex measurement methods to predicting model-based observational results to mitigating risk for future observatories by enabling effective pipeline testing. We thus advocate for funding for facilities to provide robust data simulators and publicly archive synthetic data.

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