FastPM: Fast Pretrained Model for Cosmological Simulation-Based Inference

Modern cosmology increasingly relies on large ensembles of expensive simulations to model the non-linear Universe and extract information from upcoming surveys. The new analysis still awaits peer review, but it already lays out the central claim clearly.

It is relevant because cosmology operates at the edge of what current instruments can measure, where systematic errors and model assumptions are never trivial. Small discrepancies between independent measurements have historically pointed toward missing physics rather than simple calibration errors, and the ongoing tension in the Hubble constant is a live example of how a persistent disagreement between methods can reshape the theoretical landscape. Each new dataset that approaches this territory with independent systematics adds real information to a problem that has resisted easy resolution for more than a decade. However, generating high-fidelity simulations---whether gravity-only N-body or full hydrodynamical---requires substantial computational resources. In this work, we demonstrate that transfer learning from fast low-fidelity simulations can dramatically reduce the cost of training machine learning models, in particular for.

We train a Fast Pretrained Model (FastPM) on a set of Fast Particle-Mesh (FastPM) simulations to infer $Ω_m$ and $σ_8$ from the power spectrum, and finetune on a smaller set of. To reach a fixed posterior accuracy, the pretrained model requires only 5--15\% of the high-fidelity simulations compared to the number of simulations needed when training on.

Remarkably, the gains persist for the total matter power spectrum of full hydrodynamical simulations: although FastPM contains no baryonic physics, pretraining on FastPM runs that. Moreover, pretraining on more accurate FastPM runs yields more accurate posteriors than training on CAMELS alone achieves with any number of simulations we consider.

These results demonstrate that transfer learning from fast simulations is a simple and powerful route to high-fidelity SBI, and highlight the promise of simulation-based. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

The relevance goes beyond one dataset because even small shifts in measured parameters can matter when the field is testing the limits of the standard cosmological model. The Lambda-CDM framework describes the observable universe with remarkable economy, but its success rests on two components, dark matter and dark energy, whose physical nature remains entirely unknown. Any credible measurement that tightens or loosens the constraints on those components moves the entire theoretical enterprise forward, regardless of whether the immediate result looks dramatic on its own terms.

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Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is to see whether the effect survives when independent surveys, different calibration strategies and tighter control of systematic uncertainties enter the picture. Programmes such as Euclid, DESI and the Rubin Observatory will deliver datasets over the next several years that cover the same parameter space with largely independent methods. If the current signal persists through those tests, its theoretical implications will become impossible to set aside. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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