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Title: Predicting Stellar Masses of the First Galaxies Using Graph Neural Networks
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.  more » « less
Award ID(s):
2108020
PAR ID:
10501996
Author(s) / Creator(s):
; ;
Publisher / Repository:
DOI PREFIX: 10.3847
Date Published:
Journal Name:
Research Notes of the AAS
Volume:
8
Issue:
4
ISSN:
2515-5172
Format(s):
Medium: X Size: Article No. 108
Size(s):
Article No. 108
Sponsoring Org:
National Science Foundation
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