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We use deep learning to explore the nature of observed giant clumps inhigh-redshift disc galaxies, based on their identification and classificationin cosmological simulations. Simulated clumps are detected using the 3D gas andstellar densities in the VELA zoom-in cosmological simulation suite, with $\sim\!\! 25\ \!\rm{pc}$ maximum resolution, targeting main sequence galaxies at$1\!<\!z\!<\!3$. The clumps are classified as long-lived clumps (LLCs) orshort-lived clumps (SLCs) based on their longevity in the simulations. We thentrain neural networks to detect and classify the simulated clumps in mock,multi-color, dusty and noisy HST-like images. The clumps are detected using anencoder-decoder convolutional neural network (CNN), and are classifiedaccording to their longevity using a vanilla CNN. Tests using the simulationsshow our detector and classifier to be $\sim80\%$ complete and $\sim80\%$ purefor clumps more massive than $\sim10^{7.5}\rm{M_\odot}$. When applied toobserved galaxies in the CANDELS/GOODS S+N fields, we find both types of clumpsto appear in similar abundances in the simulations and the observations. LLCsare, on average, more massive than SLCs by $\sim 0.5\ \rm{dex}$, and theydominate the clump population above $M_{\rm c}\gtrsim 10^{7.6}\ \rm{M_\odot}$.LLCs tend to be found closer to the galactic centre, indicating clump migrationto the centre or preferential formation at smaller radii. The LLCs are found toreside in high mass galaxies, indicating better clump survivability undersupernova feedback there, due to clumps being more massive in these galaxies.We find the clump properties in the simulations and the observations to agreewithin a factor of $\sim\! 2$.

The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations