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arXiv · 2607.29426

Visual analytics for cosmological simulation results

Paul Vauterin·Maarten Baes
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Abstract

Context: Modern cosmological simulations rely on sophisticated assumptions and generate rich, highly complex datasets. Interpreting their results in order to extract new scientific insights remains challenging. Existing visualisation tools offer powerful capabilities but often come with a relatively high barrier for entry, and are not designed with a focus on knowledge discovery through real-time, intuitive visual analytics. This stands in contrast to other research domains, such as bioinformatics, where visual analytics tools have become deeply embedded in scientific discovery workflows. Aim: Our goal is to complement the existing ecosystem of cosmological visualisation tools with a lightweight, user-friendly application that supports visual analytics and frictionless dissemination of results to the scientific community. Methods: We developed ARGOS, an open-source, web-based environment for real-time visual analytics, tailored to cosmological simulation outputs and designed with an emphasis on user experience. ARGOS combines GPU-accelerated browser rendering for interactive exploration of large datasets with a template-driven approach that enables rapid adaptation to other types of data and analysis workflows. Importantly, ARGOS combines catalogue-level and object-level exploration, allowing users to move seamlessly from large ensembles of objects to snapshots of individual objects. Results: We demonstrate how ARGOS can support intuitive visual data exploration through example dashboards for multiple cosmological particle datasets, and illustrate broader applicability with dashboards for SKIRT synthetic multi-band imaging data products. Source code is freely available at https://github.com/pvaut/skirt-argos under the MIT licence. A reference deployment, including sample data, is available at https://skirt-argos.ugent.be.

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Paul Vauterin, Maarten Baes. 2026-07-31. Visual analytics for cosmological simulation results. https://doi.org/10.1051/0004-6361%2F202660853

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