The Tool Desk
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Where to find credible simulations and visualizations
Official project and agency pages are useful starting points because they can connect media to its scientific context, identify creators, and point to methods or data. These examples serve different purposes; they are not a ranking of which simulation is most reliable.
- NASA Scientific Visualization Studio: NASA’s Galaxy Formation page provides video and images of a supercomputer simulation depicting Milky Way-like galaxy formation over 13.7 billion years. The page credits Brian O’Shea and Michael Norman and identifies Matt Davenport as the writer. The stated duration describes the simulation presentation, not an age measurement derived from it.
- Illustris: The project site links project information, media, public data access, and interactive exploration. The original Illustris simulation was completed in late 2013, so distinguish it from successor or related work. Its own background and caveats discuss discrepancies in the original implementation, including in star-formation-rate density and the stellar mass function. Do not assume those particular caveats apply to IllustrisTNG or another suite.
- EAGLE: The collaboration’s public catalogue release paper describes halo and galaxy catalogues and simulation boxes ranging from 25 to 100 comoving megaparsecs per side. Those figures describe that release, not a guarantee of current data availability. The project paper discusses limits on predictive power, including finite resolution and poorly constrained subgrid physics.
- FOGGIE: NASA’s 2021 project overview describes design choices aimed at studying gas flows and stellar halos, including high gas resolution in diffuse surroundings, relatively low-mass stellar particles, and closely spaced outputs. It reports approximately 50–100 million gas resolution elements and about 100 million stellar particles per simulation. These are project-specific scale details, not a reliability score. The page also reports a historical compute cost of about 12–18 months on 512 cores per simulation; compute time alone does not establish scientific quality.
- ChaNGA: NASA’s project overview describes a model that includes gravity, gas hydrodynamics, star formation and death, and black-hole evolution, with an approximate resolution of 100 parsecs in that account.
- FIRE-2: The collaboration’s physics and numerics paper explains how physical modeling and numerical resolution interact and sets out resolution criteria. That makes it useful when assessing what a simulation can resolve versus approximate.
NASA’s older Bolshoi overview discusses interpreting observations with simulations, while another NASA page describes a simulation and Hubble comparison. These are historical project pages, not current league tables. A review of successes and challenges is available at Hydrodynamical Simulations of the Galaxy Population: Enduring Successes and Outstanding Challenges. A 2025 comparison of simulation volume and resolution appears in Astronomy & Astrophysics.
How to judge whether a simulation fits your question
“Reliable” is not a single property that can be established by a striking image or one headline number. Assess the project against the scientific question you want to explore.
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1. Identify the target scale
Ask whether the work is designed to study an individual galaxy, a population of galaxies, their environments, or large-scale structure. A large cosmological volume can represent more environments and populations; a high-resolution zoom simulation can focus more closely on internal structure or gas flows. EAGLE’s release boxes and FOGGIE’s emphasis on diffuse gas and stellar halos illustrate different priorities, not a universal winner.
2. Check what the model resolves
Look for spatial and mass resolution, particle or cell counts, and how often outputs are saved. Also check what happens below the resolved scale: the simulation may represent complex processes through approximations rather than calculate each one directly. FOGGIE’s overview notes that a stellar particle represents a population of stars, which has implications for interpreting galaxies of different masses. A quoted resolution figure is meaningful only alongside the project’s target and method.
Rank #2
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3. Read which physics and assumptions are included
Gravity and gas dynamics are only part of the picture. Star formation, stellar feedback, gas cooling, black-hole feedback, and dust treatment can all affect a simulation’s outputs. Check the project paper for the processes included and the assumptions used to represent unresolved physics; NASA’s ChaNGA description, for example, names several modeled processes, while the EAGLE paper discusses uncertainty from subgrid physics.
4. Look for comparison with relevant observations
Find out which measured properties, populations, spectra, or images the project compares against. Agreement on one property does not establish that every predicted feature is accurate, but comparisons help show where a model is useful. NASA describes simulations used to interpret Hubble observations and projects that compare simulations with Hubble images. Visual resemblance by itself is not a validation test.
Rank #3
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5. Read the caveats for the specific project and version
Limitations belong to the model and release that document them. The discrepancies discussed for the original Illustris implementation should not be generalized to IllustrisTNG or other simulations. EAGLE’s project paper identifies finite resolution and poorly constrained subgrid physics as limits on predictive power. More broadly, the FIRE-2 paper addresses how numerical resolution and physical modeling constrain interpretation.
6. Check provenance and access
Prefer a page that identifies the collaboration, methods paper, data release, visualization credits, and available catalogues or tools. This makes it possible to trace an image back to the underlying project and inspect the data or documentation rather than relying on an unattributed repost.
Rank #4
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How to read a simulation image or video
A simulation is a computational model of physical processes; a visualization is a rendering of selected outputs, often with choices about color, brightness, viewpoint, and time. Those choices help make the data legible, but they are not themselves observations. NASA’s Bolshoi overview describes radiative-transfer images that incorporate wavelength, stellar-evolution, and dust effects—details that can shape what the viewer sees.
- Read the caption or project page to learn which quantities are rendered and whether the image is a direct data view or a processed, observation-like rendering.
- Check the time span, scale, and viewing perspective. A frame from a long simulation is a selected moment, not a complete record of every event.
- Keep the scientific claim narrower than the visual impression. A realistic-looking galaxy does not, on its own, show that the model reproduces nature accurately.
- Use the project’s credits and documentation when sharing media so that the rendering remains connected to its creators and scientific context.
A practical comparison checklist
When choosing between candidate projects, compare the dimensions that matter to your question rather than reducing them to a single score.
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| What to compare | What to look for | Why it matters |
|---|---|---|
| Scientific target | Individual galaxy, galaxy population, environment, or large-scale structure | The project must match the scale of the question. |
| Volume and sample | Box size, number of galaxies, or selected zoom targets | These affect sample diversity and the cosmic environments represented. |
| Resolution | Spatial and mass scales, particle or cell scale, and output spacing | These indicate which structures and changes can be followed directly and which are approximated. |
| Physics and assumptions | Gravity, gas dynamics, feedback, star formation, black holes, dust, or radiative transfer | Different prescriptions can change predicted results and rendered appearance. |
| Validation | Comparisons with relevant observed populations, spectra, properties, or images | It shows which aspects of the model have been tested against observations. |
| Transparency and access | Methods papers, stated caveats, data catalogues, credits, and visualization notes | These let you inspect provenance and judge the limits of an image or claim. |
NASA’s historical comparison page names realistic cosmological initial conditions, higher resolution, and greater astrophysical realism as dimensions of improvement. They are useful comparison axes, not a current ranking of projects.
Quick Recap
Choosing a starting point
- For a credited, accessible galaxy-formation video or image, begin with NASA’s Scientific Visualization Studio page.
- For public data and interactive exploration, begin with the Illustris project site and confirm which project generation or release you are viewing.
- For catalogue-based analysis, consult the EAGLE public-release paper and check the release information for data availability.
- For questions centered on diffuse gas, gas flows, or stellar halos, read FOGGIE’s design description and its resolution context.
- For a deeper assessment of numerical limits or modeling assumptions, consult the relevant collaboration papers rather than relying on a visualization page alone.
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