activity
20172023
most citedImproving galaxy morphologies for SDSS with Deep Learning

379 citations · 422 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.IM2023★ 28 cited

Identification of tidal features in deep optical galaxy images with Convolutional Neural Networks

H. Domínguez Sánchez, G. Martin, I. Damjanov +14

Interactions between galaxies leave distinguishable imprints in the form of tidal features which hold important clues about their mass assembly. Unfortunately, these structures are…

astro-ph.GA2023

Revisiting the SFR-Mass relation at z=0 with detailed deep learning based morphologies

Helena Domínguez Sánchez, Mariangela Bernardi, Marc Huertas-Company

Galaxy morphology is a key parameter in galaxy evolution studies. The enormous number of galaxies which current and future surveys will observe demand of automated methods for morp…

astro-ph.CO2023★ 4 cited

On the nature of disks at high redshift seen by JWST/CEERS with contrastive learning and cosmological simulations

J. Vega-Ferrero, M. Huertas-Company, L. Costantin +26

Visual inspections of the first optical rest-frame images from JWST have indicated a surprisingly high fraction of disk galaxies at high redshifts. Here, we alternatively apply sel…

astro-ph.SR2023

J-PLUS: Towards an homogeneous photometric calibration using Gaia BP/RP low-resolution spectra

C. López-Sanjuan, H. Vázquez Ramió, K. Xiao +21

We present the photometric calibration of the twelve optical passbands for the Javalambre Photometric Local Universe Survey (J-PLUS) third data release (DR3), comprising 1642 point…

astro-ph.GA2022★ 11 cited

Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks

Ting-Yun Cheng, H. Domínguez Sánchez, J. Vega-Ferrero +54

We compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised…

astro-ph.GA2017★ 379 cited

Improving galaxy morphologies for SDSS with Deep Learning

H. Domínguez Sánchez, M. Huertas-Company, M. Bernardi +2

We present a morphological catalogue for 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 here…