Astronomers use computer simulations as virtual experiments: they begin with conditions inferred for the early universe, calculate how gravity and modeled astrophysical processes shape matter over time, then test the results against telescope observations. A simulation is not a recording of the past or a photograph of a real galaxy; it is a scientific model that makes predictions researchers can compare with evidence.
How do astronomers use computer simulations to study galaxy formation?
Astronomers cannot create a galaxy in a laboratory and watch its full history unfold. Instead, they specify a model of the universe’s starting conditions, the laws and processes to include, and numerical methods for calculating their effects. The computer then advances the model through time, producing predictions about how structures and galaxies form and change. NASA describes this approach as using hydrodynamic simulations to start from early-universe conditions and predict galaxy evolution (NASA, “Galaxy Simulations Take On Ancient Questions,” published 2014 and updated 2022).
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That process is a virtual experiment, not a controlled experiment in the laboratory: researchers can change assumptions or parameters and see how the model’s predictions change. As astrophysicist Renyue Cen, principal investigator of the project described by NASA, put it: “But because we cannot contain galaxy-scale experiments in the lab, we do virtual experiments with simulations, using NASA supercomputers,” (NASA).
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What goes into a galaxy simulation?
Galaxy formation links phenomena that operate at very different scales. Gravity gathers matter into large structures; gas moves, heats and cools; stars form; and processes associated with stars and black holes can affect their surroundings. NASA calls the task a “multi-scale, multi-physics computational problem” (NASA Advanced Supercomputing, project page last updated 2020).
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A simulation cannot directly calculate every relevant process at every scale. Its spatial and mass resolution—the smallest structures or masses it can represent—constrain what can be followed explicitly. Teams therefore use numerical methods suited to their questions, and often represent unresolved processes with sub-grid models or calibrated efficiencies. Illustris discusses sub-grid modeling and numerical-method development; the EAGLE project describes calibrating feedback efficiencies against observed galaxy properties (Illustris; EAGLE).
Which simulation methods do astronomers use?
Methods differ in how they handle ordinary, or baryonic, matter such as gas and stars, and in the scale of question they can address. None is universally best: the useful choice depends on whether researchers need gravitational structure, detailed gas behavior, an individual galaxy, or a large population.
| Approach | What it models | Trade-off or use |
|---|---|---|
| Dark-matter-only N-body | Gravitational evolution of dark matter particles | Efficient for tracing large-scale structure, but needs an additional galaxy-formation model to predict visible galaxy properties. |
| Semi-analytical model | Prescriptions for baryonic processes applied to dark-matter simulation results, often in post-processing | Connects dark-matter structures to galaxy properties without directly resolving gas dynamics in the same way as a hydrodynamic simulation. |
| Hydrodynamic simulation | Gravitational structure and gas evolution using computational-fluid-dynamics methods | Models baryonic components in more detail, typically at higher computational cost. |
| Zoom-in study | A selected galaxy or small number of galaxies at enhanced resolution | Useful for detailed questions about particular systems, rather than representing a very large population. |
| Large-volume suite | A broad volume containing many galaxies | Supports population-level comparisons, while trading some local detail for representative samples. |
These categories can describe different aspects of a project: for example, a hydrodynamic calculation may use a zoom-in design. Illustris summarizes dark-matter-only, semi-analytical and hydrodynamic approaches, along with the trade-offs between simulation volume and resolution (Illustris Project, “About”).
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How are simulation results tested against observations?
A simulation becomes scientifically useful when its predictions are compared with observations. Researchers may test statistical properties of simulated galaxy populations against measured galaxy properties. They can also turn model outputs into synthetic images or spectra and compare those products with telescope data. Agreement supports the model’s usefulness for the tested evidence; it does not prove that every modeled process or assumption is uniquely correct.
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Synthetic observing is a forward-modeling step, not a way to turn a simulation into a real telescope exposure. A NASA project describes generating simulated images and spectra that incorporated stellar evolution and dust scattering and absorption, then comparing them with Hubble images (NASA Advanced Supercomputing, project page last updated 2015). The comparison asks whether a model, viewed through the effects it predicts, can reproduce relevant observed features.
What do Illustris, EAGLE and FOGGIE show?
Illustris: connecting cosmological structure to galaxies
Illustris describes hydrodynamic simulations as one way to link cosmological structure to galaxies, with results compared against observational constraints. Its account also highlights why numerical methods and sub-grid prescriptions matter: the calculation must represent processes that cannot all be resolved directly (Illustris Project, “About”).
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EAGLE: calibrating feedback and checking multiple properties
The EAGLE project describes a large-scale hydrodynamic campaign focused on galaxy formation and gaseous environments. It reports calibrating several feedback efficiencies against the observed galaxy stellar-mass function, the black-hole/galaxy mass relation and galaxy sizes. Those are calibration targets, so their agreement is not independent confirmation that the chosen feedback prescription is the only correct explanation. The EAGLE site reports that its largest simulation contained 6.8 billion particles; that is a project-specific figure, not a universal record or current benchmark (EAGLE Project, “The EAGLE Simulations”).
FOGGIE: modeling gas around Milky Way-like galaxies
NASA describes FOGGIE simulations using Enzo adaptive mesh refinement to model gas and stellar halos around Milky Way-like galaxies, interpret Hubble data and make predictions for observations. The project page reports six modeled galaxies as an example of that work, not a statement of the project’s current total. NASA reported that each described run used 512 cores for 12 to 18 months of wall-clock time and included tens of millions of resolution elements and about 100 million stellar particles. Those figures describe the specific historical FOGGIE runs, not a general cost for galaxy simulations (NASA Advanced Supercomputing, project page last updated 2021).
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Why do these calculations need supercomputers?
A calculation may need to follow many interacting elements across a large range of scales, and researchers often run multiple models to explore how assumptions affect predictions. The computational demand can be substantial, as can the effort to analyze the resulting data. NASA reports that the particular runs described for FOGGIE generated terabytes of data; the same project page estimates about 1,000 processor-hours for its described visualization treatment. Neither number should be taken as a general benchmark for other simulations or visualizations (NASA Advanced Supercomputing, 2021).
Likewise, NASA’s reported dynamic ranges—more than 6 orders of magnitude in spatial scale and more than 10 orders of magnitude in mass—refer to the adaptive-mesh-refinement simulations on its project page, not all galaxy simulations (NASA Advanced Supercomputing, page last updated 2020).
What can a simulation establish—and what remains uncertain?
Simulations let astronomers test whether a set of physical assumptions and numerical choices can produce galaxies whose predicted properties resemble observed ones. That is powerful evidence, but the result is conditional on what the model includes, what its resolution can capture, and how unresolved phenomena are represented. When a model matches one chosen property—especially one used to calibrate it—that match alone does not establish that its underlying explanation is uniquely right.
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