6 papers
ProMage: fast galaxy magnitudes emulation combining SED forward-modelling and machine learning
Luca Tortorelli, Silvan Fischbacher, Aaron S. G. Robotham +2
We present ProMage, a feed-forward neural network that emulates the computation of observer- and rest-frame magnitudes from the generative galaxy SED package ProSpect. The network…
GalSBI-SPS: a stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications
Luca Tortorelli, Silvan Fischbacher, Daniel Grün +4
Next generation photometric and spectroscopic surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting…
SHAM-OT: Rapid Subhalo Abundance Matching with Optimal Transport
Silvan Fischbacher, Tomasz Kacprzak, Luis Fernando Machado Poletti Valle +1
Subhalo abundance matching (SHAM) is widely used for connecting galaxies to dark matter haloes. In SHAM, galaxies and (sub-)haloes are sorted according to their mass (or mass proxy…
galsbi: A Python package for the GalSBI galaxy population model
Silvan Fischbacher, Beatrice Moser, Tomasz Kacprzak +5
Large-scale structure surveys measure the shapes and positions of millions of galaxies in order to constrain the cosmological model with high precision. The resulting large data vo…
UFig v1: The ultra-fast image generator
Silvan Fischbacher, Beatrice Moser, Tomasz Kacprzak +8
With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. is a public Python package that simulates astronomic…
GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference
Silvan Fischbacher, Tomasz Kacprzak, Luca Tortorelli +4
We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizatio…