ERGO-ML: towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps
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 observable galaxy properties and merger histories is not trivial: cosmological galaxy simulations are the only tools we have for calibration. We study the robustness of a
Angeloudi, Eirini et al.
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8
2023