The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations
Omri Ginzburg, Marc Huertas-Company, Avishai Dekel, Nir Mandelker,, Gregory Snyder, Daniel Ceverino, Joel Primack

TL;DR
This study employs deep learning to compare the properties and longevity of giant clumps in high-redshift galaxies between cosmological simulations and observations, revealing similarities and insights into clump evolution.
Contribution
It introduces a neural network-based method to detect and classify giant clumps in simulated and observed galaxies, linking clump properties with their longevity and galactic environment.
Findings
Clumps are detected with ~80% completeness and purity for masses >10^7.5 M_sun.
Long-lived clumps are more massive and closer to galaxy centers.
Simulation and observation clump properties agree within a factor of two.
Abstract
We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar densities in the VELA zoom-in cosmological simulation suite, with maximum resolution, targeting main sequence galaxies at . The clumps are classified as long-lived clumps (LLCs) or short-lived clumps (SLCs) based on their longevity in the simulations. We then train neural networks to detect and classify the simulated clumps in mock, multi-color, dusty and noisy HST-like images. The clumps are detected using an encoder-decoder convolutional neural network (CNN), and are classified according to their longevity using a vanilla CNN. Tests using the simulations show our detector and classifier to be complete…
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