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Creators/Authors contains: "Sethuram, Snigdaa"

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  1. ; ; (, Research Notes of the AAS)
    Abstract Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes O ( 10 6 ) core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires O ( 10 3 ) core-hours foregoing directly simulating internal structure for computational efficiency. In this work, we present a proof-of-concept machine learning regression model, using a graph neural network architecture, to predict the stellar mass of high-redshift galaxies solely from their dark matter merger trees, trained from a radiation hydrodynamics cosmological simulation of the first galaxies. 
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  2. ; ; ; ; ; ; ; ; ; ; et al (, The Astrophysical Journal Supplement Series)
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