On the nature of disks at high redshift seen by JWST/CEERS with contrastive learning and cosmological simulations

Visual inspections of the first optical rest-frame images from JWST have indicated a surprisingly high fraction of disk galaxies at high redshifts. Here we alternatively apply self-supervised machine learning to explore the morphological diversity at $z \geq 3$. Our proposed data-driven representation scheme of galaxy morphologies, calibrated on mock images from the TNG50 simulation, is shown to be robust to noise and to correlate well with physical properties of the simulated galaxies, including their 3D structure. We apply the method simultaneously to F200W and F356W galaxy images of a mass-complete sample ($M_*/M_\odot>10^9$) at $z \geq 3$ from the first JWST/NIRCam CEERS data release. We find that the simulated and observed galaxies do not populate the same manifold in the representation space from contrastive learning, partly because the observed galaxies tend to be more compact and more elongated than the simulated galaxies. We also find that about half the galaxies that were visually classified as disks based on their elongated images actually populate a similar region of the representation space than spheroids, which according to the TNG50 simulation is occupied by objects with low stellar specific angular momentum and non-oblate structure. This suggests that the disk fraction at $z > 3$ as evaluated by visual classification may be severely overestimated by misclassifying compact, elongated galaxies as disks. Deeper imaging and/or spectroscopic follow-ups as well as comparisons with other simulations will help to unambiguously determine the true nature of these galaxies.Comment: 25 pages, 23 figures. Submitted to ApJ. Comments welcom