Feedback-free starbursts at cosmic dawn. Observable predictions for JWST

We extend the analysis of a physical model within the standard cosmology that robustly predicts a high star-formation efficiency (SFE) in massive galaxies at cosmic dawn due to feedback-free starbursts (FFBs). This model implies an excess of bright galaxies at $z 10$ compared to the standard models based on the low SFE at later epochs, an excess that is indicated by JWST observations. Here we provide observable predictions of galaxy properties based on the analytic FFB scenario. These can be compared with simulations and JWST observations. We use the model to approximate the SFE as a function of redshift and mass, assuming a maximum SFE of $ max 1$ in the FFB regime. From this, we derive the evolution of the galaxy mass and luminosity functions as well as the cosmological evolution of stellar and star-formation densities. We then predict the star-formation history (SFH), galaxy sizes, outflows, gas fractions, metallicities, and dust attenuation, all as functions of mass and redshift in the FFB regime. The major distinguishing feature of the model is the occurrence of FFBs above a mass threshold that declines with redshift. The luminosities and star formation rates in bright galaxies are predicted to be in excess of extrapolations of standard empirical models and standard cosmological simulations, an excess that grows from $z 9$ to higher redshifts. The FFB phase of $ is predicted to show a characteristic SFH that fluctuates on a timescale of $ The stellar systems are compact ($ at $z 10$ and declining with $z$). The galactic gas consists of a steady wind driven by supernovae from earlier generations, with high outflow velocities FWHM low gas fractions ($<\!0.1$), low metallicities ($ and low dust attenuation UV 0.5$ at $z 10$ and declining with $z$). We make tentative comparisons with current JWST observations for initial insights, anticipating more complete and reliable datasets for detailed quantitative comparisons in the future. The FFB predictions are also offered in digital form.