28 citations · 39 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
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…
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…