14 citations · 33 across the 9 of their papers we have counts for
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astro-ph.GA2023★ 14 cited
ERGO-ML: Comparing IllustrisTNG and HSC galaxy images via contrastive learning
Lukas Eisert, Connor Bottrell, Annalisa Pillepich +5
Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the…
astro-ph.GA2023★ 14 cited
ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps
Eirini Angeloudi, Jesús Falcón-Barroso, Marc Huertas-Company +4
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between o…