  The influx of massive amounts of data from current and upcoming cosmological
surveys necessitates compression schemes that can efficiently summarize the
data with minimal loss of information. We introduce a method that leverages the
paradigm of self-supervised machine learning in a novel manner to construct
representative summaries of massive datasets using simulation-based
augmentations. Deploying the method on hydrodynamical cosmological simulations,
we show that it can deliver highly informative summaries, which can be used for
a variety of downstream tasks, including precise and accurate parameter
inference. We demonstrate how this paradigm can be used to construct summary
representations that are insensitive to prescribed systematic effects, such as
the influence of baryonic physics. Our results indicate that self-supervised
machine learning techniques offer a promising new approach for compression of
cosmological data as well its analysis.
