Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using Machine Learning
M. Huertas-Company, Y. Guo, O. Ginzburg, C.T. Lee, N. Mandelker, M., Metter, J.R. Primack, A. Dekel, D. Ceverino, S.M. Faber, D.C. Koo, A., Koekemoer, G. Snyder, M. Giavalisco, H. Zhang

TL;DR
This study introduces a neural network-based method to detect and analyze giant star-forming clumps in high-redshift galaxies, revealing their mass distribution and formation mechanisms through combined observational and simulated data.
Contribution
It presents a novel neural network approach for clump detection and provides a comprehensive comparison of observed and simulated clump properties, accounting for observational biases.
Findings
Clump stellar mass function follows a power-law down to 10^7 solar masses.
Observational effects can overestimate clump masses by up to a factor of 10.
Simulations reproduce the shape of the observed clump mass function and clumpy fractions.
Abstract
A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of a complete sample of star forming galaxies at in the CANDELS survey as well as in images from the VELA zoom-in cosmological simulations. We show that observational effects have a dramatic impact on the derived clump properties leading to an overestimation of the clump mass up to a factor of 10, which highlights the importance of fair comparisons between observations and simulations and the limitations of current HST data to study the resolved structure of distant galaxies. After correcting for these effects with a mixture…
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