COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos
By B. Diemer. Astrophysical Journal Supplement Series, 2017
311 citations recorded by the source corpus.
This paper introduces Colossus, a public, open-source python package for calculations related to cosmology, the large-scale structure (LSS) of matter in the universe, and the properties of dark matter halos. The code is designed to be fast and easy to use, with a coherent, well-documented user interface. The cosmology module implements Friedman–Lemaitre–Robertson–Walker cosmologies including curvature, relativistic species, and different dark energy equations of state, and provides fast computations of the linear matter power spectrum, variance, and correlation function. The LSS module is concerned with the properties of peaks in Gaussian random fields and halos in a statistical sense, including their peak height, peak curvature, halo bias, and mass function. The halo module deals with spherical overdensity radii and masses, density profiles, concentration, and the splashback radius. To facilitate the rapid exploration of these quantities, Colossus implements more than 40 different fitting functions from the literature. I discuss the core routines in detail, with particular emphasis on their accuracy. Colossus is available at bitbucket.org/bdiemer/colossus.
Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics
View Paper: https://www.semanticscholar.org/paper/28ecc2a60587881331d489fcc8fe3c6b222eb585
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Astronomy and Astrophysics
In Nuclear and High Energy Physics
Citations in the corpus
- Modeling the Radio Background from the First Black Holes at Cosmic Dawn: Implications for the 21 cm Absorption Amplitude (2018, methodology): “Calculations in this work were performed using the Colossus library (Diemer 2017).”
- An Accurate Physical Model for Halo Concentrations (2018, methodology): “The new best-fit parameters are listed in Table 3 and have been implemented in the COLOSSUS code (though the original parameter set is also available for backward compatibility).”
- Warm dark matter chills out: constraints on the halo mass function and the free-streaming length of dark matter with eight quadruple-image strong gravitational lenses (2019, methodology): “Cosmological computations involving the halo mass function and the matter power spectrum are performed with COLOSSUS (Diemer 2018).”
- nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure (2017, methodology): “We note that modular, object-oriented designs using Python are becoming more popular recently, e.g., astropy, the yt project (Turk et al. 2011), Halotools (Hearin et al. 2017), and Colossus (Diemer 2017).”