87 citations · 119 across the 2 of their papers we have counts for
6 papers
SDSS-IV MaNGA: drivers of stellar metallicity in nearby galaxies
Justus Neumann, Daniel Thomas, Claudia Maraston +12
The distribution of stellar metallicities within and across galaxies is an excellent relic of the chemical evolution across cosmic time. We present a detailed analysis of spatially…
SDSS-IV MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs and implications for bulge properties and stellar angular momentum
J. -L. Fischer, H. Domínguez Sánchez, M. Bernardi
We describe the SDSS-IV MaNGA PyMorph Photometric (MPP-VAC) and MaNGA Deep Learning Morphology (MDLM-VAC) Value Added Catalogs. The MPP-VAC provides photometric parameters from Sér…
Transfer learning for galaxy morphology from one survey to another
H. Domínguez Sánchez, M. Huertas-Company, M. Bernardi +56
Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorit…
A simultaneous search for High- LAEs and LBGs in the SHARDS survey
P. Arrabal Haro, J. M. Rodríguez Espinosa, C. Muñoz-Tuñón +15
We have undertaken a comprehensive search for both Lyman Alpha Emitters (LAEs) and Lyman Break Galaxies (LBGs) in the SHARDS Survey of the GOODS-N field. SHARDS is a deep imaging s…
Star-forming galaxies at low-redshift in the SHARDS survey
A. Lumbreras-Calle, C. Muñoz-Tuñón, J. Méndez-Abreu +13
The physical processes driving the evolution of star formation (SF) in galaxies over cosmic time still present many open questions. Recent galaxy surveys allow now to study these p…
Deep learning for galaxy surface brightness profile fitting
D. Tuccillo, M. Huertas-Company, E. Decencière +3
Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy mo…