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20142018
most citedImproving galaxy morphologies for SDSS with Deep Learning

379 citations · 1.2k across the 10 of their papers we have counts for

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Showing 2017Show all

5 papers · 1 filter

astro-ph.GA2017★ 33 cited

gradients driven by IMF variation: Large impact on dynamical stellar mass estimates

M. Bernardi, R. K. Sheth, H. Dominguez-Sanchez +4

Within a galaxy the stellar mass-to-light ratio is not constant. Spatially resolved kinematics of nearby early-type galaxies suggest that allowing for a variable initial mass…

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…

astro-ph.GA2017★ 28 cited

Stellar mass functions and implications for a variable IMF

M. Bernardi, R. K. Sheth, J. -L. Fischer +6

Spatially resolved kinematics of nearby galaxies has shown that the ratio of dynamical- to stellar population-based estimates of the mass of a galaxy () correlat…

astro-ph.GA2017★ 32 cited

Comparing PyMorph and SDSS photometry. II. The differences are more than semantics and are not dominated by intracluster light

M. Bernardi, J. -L. Fischer, R. K. Sheth +4

The Sloan Digital Sky Survey pipeline photometry underestimates the brightnesses of the most luminous galaxies. This is mainly because (i) the SDSS overestimates the sky background…

astro-ph.GA2017★ 29 cited

Comparing PyMorph and SDSS photometry. I. Background sky and model fitting effects

J. -L. Fischer, M. Bernardi, A. Meert

A number of recent estimates of the total luminosities of galaxies in the SDSS are significantly larger than those reported by the SDSS pipeline. This is because of a combination o…