Probing the faint-end of simulated galaxy counts at z>3

Simulations and observations now probe comparable redshift regimes with unprecedented accuracy, enabling direct consistency tests through forward modeling. The new analysis still awaits peer review, but it already lays out the central claim clearly.

This matters 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. In a previous work, we identified a faint-end discrepancy between observed and simulated near-infrared galaxy counts in CANDELS GOODS-South. Here we investigate whether this tension originates from the forward-modeling procedure or from limitations of the underlying simulations, and we characterize the galaxy.

Using the FORECAST forward modeling code, we generated ten independent lightcone realizations and mock CANDELS images from the TNG100 and EAGLE simulations. We compared both the intrinsic lightcone catalogs and the mock-image detections with observations, testing dependencies on field and redshift, and validating the pipeline through.

The faint-end deficit is present in all CANDELS fields and appears at z>3 in both simulations. GOODS-South counts corrected for completeness exceed intrinsic simulation counts already at the 50% completeness limit, indicating that the missing population is not simply hidden.

Increasing the depth of mock images recovers the counts near the peak but overpredicts the faintest sources, showing that depth alone cannot resolve the discrepancy. Structural analyses reveal that compact galaxies with bright central cores observed in GOODS-South are underproduced in simulations, which instead favor diffuse.

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.

We conclude that the discrepancy arises both from detection losses of diffuse galaxies and, more fundamentally, from the inability of current hydrodynamical simulations to produce. This tension points to the need for improved modeling of early star formation, feedback, and dust treatment.

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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