9 citations · 15 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
Laura Sommovigo, Deaglan J. Bartlett, Rachel K. Cochrane +3
Dust attenuation is a major source of systematic uncertainty in both SED fitting and forward modeling of galaxy populations, yet the functional form used to parameterize attenuatio…
Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer
Thomas Harvey, Christopher C. Lovell, Sophie Newman +13
We introduce Synference, a new, flexible Python framework for galaxy SED fitting using simulation-based inference (SBI). Synference leverages the Synthesizer package for flexible f…
Learning the Universe: physically-motivated priors for dust attenuation curves
Laura Sommovigo, Rachel K. Cochrane, Rachel S. Somerville +9
Understanding the impact of dust on the spectral energy distributions (SEDs) of galaxies is crucial for inferring their physical properties and for studying the nature of interstel…
Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho +9
We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference ap…