  We present the results of a machine learning study to measure the dust
content of galaxies observed with JWST at z > 6 through the use of trained
neural networks based on high-resolution IllustrisTNG simulations. Dust is an
important unknown in the evolution and observability of distant galaxies and is
degenerate with other stellar population features through spectral energy
fitting. As such, we develop and test a new SED-independent machine learning
method to predict dust attenuation and sSFR of high redshift (z > 6) galaxies.
Simulated galaxies were constructed using the IllustrisTNG model, with a
variety of dust contents parameterized by E(B-V) and A(V) values, then used to
train Convolutional Neural Network (CNN) models using supervised learning
through a regression model. We demonstrate that within the context of these
simulations, our single and multi-band models are able to predict dust content
of distant galaxies to within a 1$\sigma$ dispersion of A(V) $\sim 0.1$.
Applied to spectroscopically confirmed z > 6 galaxies from the JADES and CEERS
programs, our models predicted attenuation values of A(V) < 0.7 for all
systems, with a low average (A(V) = 0.28). Our CNN predictions show larger dust
attenuation but lower amounts of star formation compared to SED fitted values.
Both results show that distant galaxies with confirmed spectroscopy are not
extremely dusty, although this sample is potentially significantly biased. We
discuss these issues and present ideas on how to accurately measure dust
features at the highest redshifts using a combination of machine learning and
SED fitting.
