Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS

The 3D geometry of high-redshift galaxies remains poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the 3D shapes of star-forming galaxies in JWST-CEERS observations with log M_*/M_⊙=9.0-10.5 at z=0.5-8.0. We reproduce previous results from HST-CANDELS in a fraction of the computing time and constrain the mean ellipticity, triaxiality, size and covariances with samples as small as ∼50 galaxies. We find high 3D ellipticities for all mass-redshift bins suggesting oblate (disky) or prolate (elongated) geometries. We break that degeneracy by constraining the mean triaxiality to be ∼1 for log M_*/M_⊙=9.0-9.5 dwarfs at z>1 (favoring the prolate scenario), with significantly lower triaxialities for higher masses and lower redshifts indicating the emergence of disks. The prolate population traces out a “banana” in the projected b/a-log a diagram with an excess of low b/a, large log a galaxies. The dwarf prolate fraction rises from ∼25% at z=0.5-1.0 to ∼50-80% at z=3-8. If these are disks, they cannot be axisymmetric but instead must be unusually oval (triaxial) unlike local circular disks. We simultaneously constrain the 3D size-mass relation and its dependence on 3D geometry. High-probability prolate and oblate candidates show remarkably similar Sérsic indices (n∼1), non-parametric morphological properties and specific star formation rates. Both tend to be visually classified as disks or irregular but edge-on oblate candidates show more dust attenuation. We discuss selection effects, follow-up prospects and theoretical implications.