Software |

GalaPy

Wed, 01 May 2024 00:00:00 +0000

Role: lead author & maintainer. Stack: C++ compute core exposed through a Python API; hybrid parallelisation (vectorised array programming, shared-memory concurrency, distributed-memory message passing).

GalaPy simulates the panchromatic emission of galaxies from X-rays to radio and performs Bayesian spectral-energy-distribution fitting. Models are generated on the fly rather than interpolated from a template library, which is what allows a peak throughput of almost 1000 SEDs per second on a single core — the fastest SED-generation tool of its kind.

The library is designed around an object-oriented architecture that keeps it portable with minimal memory overhead, and it scales from a laptop to an HPC allocation without changing the user-facing API. Recent additions include parallel sampling for Bayesian inference and a fully analytical, panchromatic AGN component.

Since release, GalaPy has been adopted by groups across Europe, Asia and South America, for work ranging from nearby resolved galaxies to JWST high-redshift candidates. Its role in my wider programme is described in my .

Presented in Astronomy & Astrophysics 685, A161 (2024) — see — with the computational design detailed in Astronomy and Computing 55, 101079 (2026): .

SCAMPy

Sat, 01 Aug 2020 00:00:00 +0000

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 .

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

T-RECS

Mon, 01 May 2023 00:00:00 +0000

Role: contributor — HI emission extension. Stack: Fortran executables with Python tooling for catalogue cross-matching. Lead author & maintainer: Anna Bonaldi (SKAO).

T-RECS produces simulated catalogues of radio sources with user-defined frequencies, sky area and depth. The original model covers radio continuum emission for the two main populations of radio galaxies — AGN and star-forming galaxies — including polarisation and clustering. It is now widely used in the radio-cosmology community for survey design and theoretical modelling.

During a research stay at SKAO Headquarters in Manchester I contributed to the second generation of the code, which extends T-RECS to HI line emission. That work builds the HI model on current HI mass function estimates, adds prescriptions to convert HI mass to integrated flux, and models source size, morphology and kinematics including rotational velocity and line width. It also introduces prescriptions that associate an HI mass with the existing continuum SFG and AGN populations, which is what makes it possible to cross-match HI and continuum catalogues and build HI × continuum simulated observations.

T-RECS supplies the radio emission properties in the full-sky mock pipeline described in my , where its populations are assigned to dark-matter haloes via SCAMPy.

The HI extension is presented in Monthly Notices of the Royal Astronomical Society 524, 993 (2023) — see .

CosmoBolognaLib

Wed, 01 Nov 2017 00:00:00 +0000

Role: contributor — void size function modelling and void-catalogue cleaning. Stack: C++ libraries with a SWIG-generated Python wrapper. Author & maintainer: Federico Marulli (Università di Bologna).

CosmoBolognaLib (CBL) is a living project that provides a common numerical environment for cosmological investigations of the large-scale structure of the Universe. Its focus is handling astronomical catalogues — real and simulated — measuring one-, two- and three-point statistics in configuration space, and running cosmological analyses.

My contribution is the set of tools for cosmic voids: the algorithm that redefines void ridges and therefore their radii, together with the model for the void size function. The cleaning procedure takes a void catalogue produced by a void finder and returns a catalogue of non-overlapping spheres, each embedding a fixed density contrast in the tracer density field — which is what makes the measured size function comparable with theoretical predictions. These functions were released in CBL v3.2 and are credited in the library’s changelog to Ronconi & Marulli (2017).

The tooling underpins the void work described in my , and was subsequently adopted in Key Projects within the Euclid Collaboration to forecast the cosmological constraining power of void statistics.

The void tools are presented in Astronomy & Astrophysics 607, A24 (2017) — see . The library itself is described in Marulli, Veropalumbo & Moresco (2016).