630 citations · 2.8k across the 23 of their papers we have counts for
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astro-ph.GA2020★ 46 cited
A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
S. Mucesh, W. G. Hartley, A. Palmese +72
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorit…
astro-ph.GA2019
Modelling the Milky Way. I -- Method and first results fitting the thick disk and halo with DES-Y3 data
A. Pieres, L. Girardi, E. Balbinot +53
We present MWFitting, a method to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) from TRILEGAL models to real data. We apply MWFitting to photometric dat…