Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

Abstract

We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.

corrfunc – a suite of blazing fast correlation functions on the CPU

“…While the Corrfunc framework was created to compute correlation functions efficiently, several other clustering statistics can benefit from such a framework. For example, the weak-lensing signal, ∆Σ (already implemented in Hearin et al 2017), counts-in-spheres, pN(r), can be efficiently computed within the Corrfunc framework, as can pair-wise velocity dispersion (Bibiano & Croton 2017). Other algorithms that require a reduction over neighbours within a fixed separation, e.g., kernel density estimation, can also be efficiently implemented on top of the Corrfunc design.…”

“…• Corrfunc options: autocorr = False • Corrfunc speed-up: 3.1 -6.0× (multi-threaded: 6.8 -130.5×) 6.4 halotools, version 0.4 (Hearin et al 2017) • Mesh code that mimics the Corrfunc algorithm in Cython. Divides the domain into rectangular cells.…”

CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST

“…In addition to the dependence of dM • /dt on black hole mass and redshift, we use conditional abundance matching (CAM) to introduce correlations between dlogM • /dt and sSFR, the specific star-formation rate of the galaxy. For each galaxy, we calculate the cumulative probability P(< sSFR|M ), and use the CAM implementation in Halotools (Hearin et al 2017) to non-parametrically correlate sSFR and λ edd , setting the correlation strength to 50%. In cosmoDC2, galaxy sSFR is tightly correlated with broadband color, such that active galaxies have bluer colors; thus our use of CAM in assigning λ edd produces synthetic catalogs in which galaxies with bluer broadband color host black holes that tend to be rapidly accreting mass.…”

New perspectives on the BOSS small-scale lensing discrepancy for the Planck ΛCDM cosmology

“…The cosmological parameters for MDR1 are Ωm = 0.27, σ8 = 0.82, h = 0.7, ns = 0.95 and Ω b h 2 = 0.023. We populate dark matter haloes in the MultiDark simulation using the halotools (v0.6) package (Hearin et al 2017). Centrals are assumed to be at rest with respect to the phase-space position of the halo and satellites are assumed to follow an NFW profile.…”