  Scientific datasets present unique challenges for machine learning-driven
compression methods, including more stringent requirements on accuracy and
mitigation of potential invalidating artifacts. Drawing on results from
compressed sensing and rate-distortion theory, we introduce effective data
compression methods by developing autoencoders using high dimensional latent
spaces that are $L^1$-regularized to obtain sparse low dimensional
representations. We show how these information-rich latent spaces can be used
to mitigate blurring and other artifacts to obtain highly effective data
compression methods for scientific data. We demonstrate our methods for short
angle scattering (SAS) datasets showing they can achieve compression ratios
around two orders of magnitude and in some cases better. Our compression
methods show promise for use in addressing current bottlenecks in transmission,
storage, and analysis in high-performance distributed computing environments.
This is central to processing the large volume of SAS data being generated at
shared experimental facilities around the world to support scientific
investigations. Our approaches provide general ways for obtaining specialized
compression methods for targeted scientific datasets.
