On the need for synthetic data and robust data simulators in the 2020s. (arXiv:1907.07184v1 [astro-ph.IM])

On the need for synthetic data and robust data simulators in the 2020s. (arXiv:1907.07184v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Peeples_M/0/1/0/all/0/1">Molly S. Peeples</a> (STScI/JHU), <a href="http://arxiv.org/find/astro-ph/1/au:+Emonts_B/0/1/0/all/0/1">Bjorn Emonts</a> (NRAO), <a href="http://arxiv.org/find/astro-ph/1/au:+Kyprianou_M/0/1/0/all/0/1">Mark Kyprianou</a> (STScI), <a href="http://arxiv.org/find/astro-ph/1/au:+Penny_M/0/1/0/all/0/1">Matthew T. Penny</a> (Ohio State), <a href="http://arxiv.org/find/astro-ph/1/au:+Snyder_G/0/1/0/all/0/1">Gregory F. Snyder</a> (STScI), <a href="http://arxiv.org/find/astro-ph/1/au:+Stark_C/0/1/0/all/0/1">Christopher C. Stark</a> (STScI), <a href="http://arxiv.org/find/astro-ph/1/au:+Troxel_M/0/1/0/all/0/1">Michael Troxel</a> (Duke), <a href="http://arxiv.org/find/astro-ph/1/au:+Zimmerman_N/0/1/0/all/0/1">Neil T. Zimmerman</a> (GSFC), <a href="http://arxiv.org/find/astro-ph/1/au:+ZuHone_J/0/1/0/all/0/1">John ZuHone</a> (Harvard-Smithsonian CfA)

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 results based on models and
astrophysical simulations for, e.g., proposal planning, and (4) mitigating risk
for future observatories and missions by effectively priming and testing
pipelines. We advocate for an increase in using synthetic data to plan for and
interpret real observations as a matter of routine. This will require funding
for (1) facilities to provide robust data simulators for their instruments,
telescopes, and surveys, and (2) making synthetic data publicly available in
archives (much like real data) so as to lower the barrier of entry to all.

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 results based on models and
astrophysical simulations for, e.g., proposal planning, and (4) mitigating risk
for future observatories and missions by effectively priming and testing
pipelines. We advocate for an increase in using synthetic data to plan for and
interpret real observations as a matter of routine. This will require funding
for (1) facilities to provide robust data simulators for their instruments,
telescopes, and surveys, and (2) making synthetic data publicly available in
archives (much like real data) so as to lower the barrier of entry to all.

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