activity
20192023
most citedPushing automated morphological classifications to their limits with the Dark Energy Survey

44 citations · 161 across the 5 of their papers we have counts for

collaborators

4 papers

astro-ph.GA2023★ 15 cited

Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Euclid Collaboration, L. Leuzzi, M. Meneghetti +216

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions…

astro-ph.GA2020★ 44 cited

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…

astro-ph.GA2020★ 33 cited

Galaxy properties as revealed by MaNGA. III. Kinematic profiles and stellar population gradients in S0s

H. Domínguez Sánchez, M. Bernardi, F. Nikakhtar +2

This is the third paper of a series where we study the stellar population gradients (SP; ages, metallicities, -element abundance ratios and stellar initial mass functions) of ea…

astro-ph.GA2019★ 36 cited

Galaxy properties as revealed by MaNGA II. Differences in stellar populations of slow and fast rotator ellipticals and dependence on environment

M. Bernardi, H. Domínguez Sánchez, J. R. Brownstein +2

We present estimates of stellar population (SP) gradients from stacked spectra of slow (SR) and fast (FR) rotator elliptical galaxies from the MaNGA-DR15 survey. We find that: 1) F…