Statistics Meet Systematics: Resolution of the Massive Early JWST Galaxy Tension

TL;DR

This paper develops a framework linking the power spectrum to galaxy growth efficiencies, accounting for uncertainties, to address tensions in early JWST galaxy observations challenging standard cosmology.

Contribution

It introduces a model that incorporates multiple uncertainties to reconcile JWST galaxy data with $ ext{Lambda}$CDM expectations.

Findings

Systematic uncertainties in stellar mass estimates significantly affect inferred efficiencies.

Accounting for biases reduces the tension with standard cosmological models.

Framework can be used to test cosmological models as data quality improves.

Abstract

The discovery of massive, high redshift galaxies with the James Webb Space Telescope (JWST) has been argued to challenge $\Lambda$CDM (cold dark matter): such systems would require extremely rare halos and baryon-to-stellar-mass conversion efficiencies unphysically approaching -- or exceeding -- $100\%$. If confirmed at galaxy-formation--forbidden efficiencies, these galaxies could signal new physics beyond standard cosmological structure formation. We develop a galaxy model framework that ties the linear power spectrum to the inferred efficiencies of galaxy growth while incorporating multiple sources of uncertainties in order to test the structure formation models. The sources of error include (i) observational sample variance, (ii) asymmetric scatter induced by the steepness of the high-mass halo tail, and (iii) systematic uncertainties in stellar mass estimates. We find that the inferred star-formation efficiency is largely controlled by systematic uncertainties in the stellar mass estimates derived from spectral energy distribution modeling of JWST-detected galaxies. Because of the inherent Eddington-like bias, systematic uncertainties amplify the asymmetry of the scatter, in some cases by orders of magnitude, thereby bringing the inferred efficiencies into closer agreement with expectations from early galaxy formation models. We also present how these uncertainties can be applied to the inferred UV luminosity function. Our framework can be used to test $\Lambda$CDM as errors are reduced and further detections are made.