A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses
Mucesh, S. et al.
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2021