January 19, 2021 article Open access A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest Monthly Notices of the Royal Astronomical Society DOI: 10.1093/mnras/stab164 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 4 stored of 47 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Euclid preparation 2025 Astronomy and Astrophysics article Physics and Astronomy Galaxies: Formation, Evolution, Phenomena Open access 2 citations Euclid preparation 2024 Astronomy and Astrophysics article Physics and Astronomy Galaxies: Formation, Evolution, Phenomena Open access 9 citations Euclid preparation 2022 Astronomy and Astrophysics article Physics and Astronomy Galaxies: Formation, Evolution, Phenomena Open access 22 citations Euclidpreparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes andH-band images 2022 Monthly Notices of the Royal Astronomical Society article Physics and Astronomy Galaxies: Formation, Evolution, Phenomena Open access 42 citations 4 results References 0