2k citations · 12.2k across the 160 of their papers we have counts for
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The Fundamental Plane of Massive Quiescent Galaxies at z~2
Mikkel Stockmann, Inger Jørgensen, Sune Toft +11
We examine the Fundamental Plane (FP) and mass-to-light ratio () scaling relations using the largest sample of massive quiescent galaxies at to date. The FP ($r_{e…
Assessing tension metrics with Dark Energy Survey and Planck data
P. Lemos, M. Raveri, A. Campos +103
Quantifying tensions -- inconsistencies amongst measurements of cosmological parameters by different experiments -- has emerged as a crucial part of modern cosmological data analys…
Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning
C. Tohill, L. Ferreira, C. J. Conselice +2
At high redshift, due to both observational limitations and the variety of galaxy morphologies in the early universe, measuring galaxy structure can be challenging. Non-parametric…
Dark Energy Survey Year 3 Results: Redshift Calibration of the Weak Lensing Source Galaxies
J. Myles, A. Alarcon, A. Amon +105
Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey is an essential component to mapping the matter den…
Pushing automated morphological classifications to their limits with the Dark Energy Survey
J. Vega-Ferrero, H. Domínguez Sánchez, M. Bernardi +60
We present morphological classifications of 27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classif…
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…