18 citations · 48 across the 3 of their papers we have counts for
3 papers
astro-ph.GA2019★ 18 cited
Predicting the global far-infrared SED of galaxies via machine learning techniques
W. Dobbels, M. Baes, S. Viaene +10
Dust plays an important role in shaping a galaxy's spectral energy distribution (SED). It absorbs ultraviolet (UV) to near-infrared (NIR) radiation and re-emits this energy in the…
astro-ph.GA2019★ 12 cited
Morphology-assisted galaxy mass-to-light predictions using deep learning
Wouter Dobbels, Serge Krier, Stephan Pirson +4
One of the most important properties of a galaxy is the total stellar mass, or equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be estimat…
astro-ph.GA2019★ 18 cited
The cosmic spectral energy distribution in the EAGLE simulation
Maarten Baes, Ana Trčka, Peter Camps +4
The cosmic spectral energy distribution (CSED) is the total emissivity as a function of wavelength of galaxies in a given cosmic volume. We compare the observed CSED from the UV to…