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Visual inspections of the first optical rest-frame images from JWST haveindicated a surprisingly high fraction of disk galaxies at high redshifts.Here, we alternatively apply self-supervised machine learning to explore themorphological diversity at $z \geq 3$. Our proposed data-driven representationscheme of galaxy morphologies, calibrated on mock images from the TNG50simulation, is shown to be robust to noise and to correlate well with thephysical properties of the simulated galaxies, including their 3D structure. Weapply the method simultaneously to F200W and F356W galaxy images of amass-complete sample ($M_*/M_\odot>10^9$) at $ 3 \leq z \leq 6$ from the firstJWST/NIRCam CEERS data release. We find that the simulated and observedgalaxies do not exactly populate the same manifold in the representation spacefrom contrastive learning. We also find that half the galaxies classified asdisks -- either CNN-based or visually -- populate a similar region of therepresentation space as TNG50 galaxies with low stellar specific angularmomentum and non-oblate structure. Although our data-driven study does notallow us to firmly conclude on the true nature of these galaxies, it suggeststhat the disk fraction at $z \geq 3$ remains uncertain and possiblyoverestimated by traditional supervised classifications. Deeper imaging andspectroscopic follow-ups as well as comparisons with other simulations willhelp to unambiguously determine the true nature of these galaxies, andestablish more robust constraints on the emergence of disks at very highredshift.

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