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We demonstrate that highly accurate joint redshift-stellar mass probabilitydistribution functions (PDFs) can be obtained using the Random Forest (RF)machine learning (ML) algorithm, even with few photometric bands available. Asan example, we use the Dark Energy Survey (DES), combined with the COSMOS2015catalogue for redshifts and stellar masses. We build two ML models: onecontaining deep photometry in the $griz$ bands, and the second reflecting thephotometric scatter present in the main DES survey, with carefully constructedrepresentative training data in each case. We validate our joint PDFs for$10,699$ test galaxies by utilizing the copula probability integral transformand the Kendall distribution function, and their univariate counterparts tovalidate the marginals. Benchmarked against a basic set-up of thetemplate-fitting code BAGPIPES, our ML-based method outperforms templatefitting on all of our predefined performance metrics. In addition to accuracy,the RF is extremely fast, able to compute joint PDFs for a million galaxies injust under $6$ min with consumer computer hardware. Such speed enables PDFs tobe derived in real time within analysis codes, solving potential storageissues. As part of this work we have developed GALPRO, a highly intuitive andefficient Python package to rapidly generate multivariate PDFs on-the-fly.GALPRO is documented and available for researchers to use in their cosmologyand galaxy evolution studies.

A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest