For over 40 years, remote sensing observations of the Earth’s oceans have yielded global measurements of sea surface temperature (SST).With a resolution of approximately 1 km, these data trace physical processes like western boundary currents, cool upwelling at eastern boundary currents, and the formation of mesoscale and sub-mesoscale eddies. To discover the fundamental patterns of SST on scales smaller than 10 km, we developed an unsupervised, deep contrastive learning model named NENYA. We trained NENYA on a subset of 8 million cloud-free cutout images (~ 80 × 80km2) from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor, with image augmentations to impose invariance to rotation, reflection, and translation. The 256-dimension latent space of NENYA defines a vocabulary to describe the complexity of SST and associates images with like patterns and features. We used a dimensionality reduction algorithm to explore cutouts with a temperature interval of ΔT = 0.5-1 K, identifying a diverse set of patterns with temperature variance on a wide range of scales. We then demonstrated that SST data with large-scale features arise preferentially in the Pacific and Atlantic Equatorial Cold Tongues and exhibit a strong seasonal variation, while data with predominantly sub-mesoscale structure preferentially manifest in western boundary currents, select regions with strong upwelling, and along the Antarctic Circumpolar Current. We provide a web-based user interface to facilitate the geographical and temporal exploration of the full MODIS dataset. Future efforts will link specific SST patterns to select dynamics (e.g., frontogenesis) to examine their distribution in time and space on the globe.

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies-called polyphorms-that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

We introduce CosmoVis, an open source web-based visualization tool for the interactive analysis of massive hydrodynamic cosmological simulation data. CosmoVis was designed in close collaboration with astrophysicists to enable researchers and citizen scientists to share and explore these datasets, and to use them to investigate a range of scientific questions. CosmoVis visualizes many key gas, dark matter, and stellar attributes extracted from the source simulations, which typically consist of complex data structures multiple terabytes in size, often requiring extensive data wrangling. CosmoVis introduces a range of features to facilitate real-time analysis of these simulations, including the use of "virtual skewers," simulated analogues of absorption line spectroscopy that act as spectral probes piercing the volume of gaseous cosmic medium. We explain how such synthetic spectra can be used to gain insight into the source datasets and to make functional comparisons with observational data. Furthermore, we identify the main analysis tasks that CosmoVis enables and present implementation details of the software interface and the client-server architecture. We conclude by providing details of three contemporary scientific use cases that were conducted by domain experts using the software and by documenting expert feedback from astrophysicists at different career levels.

This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for nutrients, astrophysicists are able to extrapolate from sparse datasets, such as galaxy maps archived in the Sloan Digital Sky Survey, and then use these extrapolations to inform analyses of a wide range of other data, such as spectroscopic observations captured by the Hubble Space Telescope. Researchers can interactively update the simulation by adjusting model parameters, and then investigate the resulting visual output to form hypotheses about the data. We describe details of Polyphorm's simulation model and its interaction and visualization modalities, and we evaluate Polyphorm through three scientific use cases that demonstrate the effectiveness of our approach.

Abstract We introduce IGM‐Vis, a novel astrophysics visualization and data analysis application for investigating galaxies and the gas that surrounds them in context with their larger scale environment, the Cosmic Web. Environment is an important factor in the evolution of galaxies from actively forming stars to quiescent states with little, if any, discernible star formation activity. The gaseous halos of galaxies (the circumgalactic medium, or CGM) play a critical role in their evolution, because the gas necessary to fuel star formation and any gas expelled from widely observed galactic winds must encounter this interface region between galaxies and the intergalactic medium (IGM). We present a taxonomy of tasks typically employed in IGM/CGM studies informed by a survey of astrophysicists at various career levels, and demonstrate how these tasks are facilitated via the use of our visualization software. Finally, we evaluate the effectiveness of IGM‐Vis through two in‐depth use cases that depict real‐world analysis sessions that use IGM/CGM data.