SCAMPy

Role: author & maintainer. Stack: C++ core (object-oriented, polymorphic) wrapped in Python.

SCAMPy implements the Sub-halo Clustering and Abundance Matching (SCAM) scheme, which combines the classical Halo Occupation Distribution with sub-halo abundance matching. The procedure runs in two steps: the HOD prescription selects which sub-haloes host galaxies, then SHAM assigns an observable property of choice to each selected sub-halo. It requires only the 1- and 2-point statistics of the target population as input — typically an observed luminosity function and its clustering — which makes the method fully data-driven rather than dependent on a physical galaxy-formation model.

The core functionalities are written in C++ and make wide use of polymorphism for flexibility and computational efficiency; the whole library is wrapped in Python so it can be driven from a scripting interface or embedded in a larger pipeline. Mock catalogues produced this way reproduce both the abundance and the clustering of the target sample, and the approach applies across a wide redshift range, from high-redshift Lyman-break galaxies to low-redshift radio sources.

SCAMPy came out of my Ph.D. thesis and has since become a core ingredient in simulation pipelines used for SKAO preparatory science, including the full-sky radio mock catalogues described in my research on the galaxy–halo connection.

Presented in Monthly Notices of the Royal Astronomical Society 498, 2095 (2020) — see the paper. Also indexed in the Astrophysics Source Code Library as ascl:2002.006.