GalaxyMC
TRY IT ONLINE HERE!
GalaxyMC is a parallelized Fortran code with a Python interface to semi-analytically model, predict and analyze high-redshift (z>4) galaxy samples.
the cloud-based computing service galaxymc.cloud is available HERE.
lead developer: Martin Sahlén, Uppsala University.
GalaxyMC is highly flexible and modular and can easily be adapted to semi-analytically model generic cosmological number counts and correlation functions of tracers of the matter distribution. Interest in using the code is gladly received.
| Functionality | Performance | ||
|---|---|---|---|
| Press-Schechter luminosity functions | luminosity function global reionization signal | Time per bin, 1 CPU Multiple CPUs | t1 ~0.01 – 0.1 s t ~ Nbins x t1 / NCPUs |
| Halo mass function + star formation rate models | luminosity function stellar mass function star formation rate duty cycles | ||
| Dust extinction | Standard parameterizations or arbitrary distribution | ||
| Selection function / contamination | Arbitrary distributions | ||
| Gravitational lensing | Weak: magnification bias, analytical models or fitted model Strong: cluster-lensed observations, arbitrary magnification distribution | ||
| Statistical uncertainties | Malmquist bias: statistical scatter in luminosity function Eddington bias: asymmetric distribution / measurement uncertainties | ||
| Full Bayesian MCMC parameter fitting | Integrated with the CosmoMC package or Python interface to use e.g. Emcee | ||
| Fisher-matrix forecast computation | |||
| Generation of mock galaxy catalogues | |||
| Combination with other astrophysical and cosmological data sets |
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