Showing 1–20 of 120 results
/ Date/ Name
Jun 8, 2023A brief review of contrastive learning applied to astrophysicsOct 4, 2022The DAWES review 10: The impact of deep learning for the analysis of galaxy surveysOct 14, 2010Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classificationDec 3, 2024The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific DataJul 24, 2012The evolution of the mass-size relation for early type galaxies from z~1 to the present: dependence on environment, mass-range and detailed morphologyDec 17, 2012No evidence for a dependence of the mass size relation of early-type galaxies on environment in the local UniverseJun 9, 2015The morphologies of massive galaxies from z~3 - Witnessing the 2 channels of bulge growthOct 22, 2021The building up of observed stellar scaling relations of massive galaxies and the connection to black hole growth in the TNG50 simulationJun 4, 2014Measuring galaxy morphology at $z>1$. I - calibration of automated proxiesMay 4, 2023Galaxy Morphology from $z\sim6$ through the eyes of JWSTFeb 16, 2010Evolution of blue E/S0 galaxies from z~1: merger remnants or disk rebuilding galaxies?Mar 18, 2019The Hubble Sequence at $z\sim0$ in the IllustrisTNG simulation with deep learningJun 15, 2016Mass assembly and morphological transformations since $z\sim3$ from CANDELSJul 22, 2009The role of environment in the morphological transformation of galaxies in 9 intermediate redshift clustersJun 25, 2020Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using Machine LearningSep 17, 2015A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learningSep 18, 2009Blue E/S0 galaxies: merger remnants or disk rebuilding galaxies?Mar 19, 2025Euclid Quick Data Release (Q1), A first look at the fraction of bars in massive galaxies at $z<1$May 3, 2019Photometry of high-redshift blended galaxies using deep learningJun 30, 2020The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view