GalaPy: A highly optimised C₊₊/Python spectral modelling tool for galaxies

I. Library presentation and photometric fitting

¹ Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
e-mail: tronconi@sissa.it
² Institute for Fundamental Physics of the Universe (IFPU), Via Beirut 2, 34151 Trieste, Italy
³ INAF – Osservatorio di Astrofisica e Scienza dello Spazio (OAS), Via Gobetti 93/3, 40127 Bologna, Italy
⁴ IRA-INAF, Via Gobetti 101, 40129 Bologna, Italy
⁵ INFN-Sezione di Trieste, Via Valerio 2, 34127 Trieste, Italy
⁶ National Centre for Nuclear Research, Pasteura 7, 02-093 Warsaw, Poland
⁷ Sterrenkundig Observatorium Universiteit Gent, Krijgslaan 281 S9, 9000 Gent, Belgium
⁸ Dip. Fisica e Astronomia ’Augusto Righi’, Univ. Bologna, Viale Berti Pichat 6/2, 40127, Bologna, Italy
⁹ INAF – Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi, 5, 50125 Firenze, FI, Italy
¹⁰ INAF-OATS, Via Tiepolo 11, 34143 Trieste, Italy
¹¹ ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing, Via Magnanelli 2, Bologna, Italy

Received: 23 May 2023
Accepted: 20 February 2024

Abstract

Bolstered by upcoming data from new-generation observational campaigns, we are about to enter a new era in the study of how galaxies form and evolve. The unprecedented quantity of data that will be collected from distances that have only marginally been grasped up to now will require analytical tools designed to target the specific physical peculiarities of the observed sources and handle extremely large datasets. One powerful method to investigate the complex astrophysical processes that govern the properties of galaxies is to model their observed spectral energy distributions (SEDs) at different stages of evolution and times throughout the history of the Universe. To address these challenges, we have developed GalaPy, a new library for modelling and fitting SEDs of galaxies from the X-ray to the radio band, as well as the evolution of their components and dust attenuation and reradiation. On the physical side, GalaPy incorporates both empirical and physically motivated star formation histories (SFHs), state-of-the-art single stellar population synthesis libraries, a two-component dust model for attenuation, an age-dependent energy conservation algorithm to compute dust reradiation, and additional sources of stellar continuum such as synchrotron, nebular and free-free emission, as well as X-ray radiation from low-and high-mass binary stars. On the computational side, GalaPy implements a hybrid approach that combines the high performance of compiled C₊₊ with the user-friendly flexibility of Python. Also, it exploits an object-oriented design via advanced programming techniques. GalaPy is the fastest SED-generation tool of its kind, with a peak performance of almost 1000 SEDs per second. The models are generated on the fly without relying on templates, thus minimising memory consumption. It exploits a fully Bayesian parameter space sampling, which allows for the inference of parameter posteriors and thereby facilitates the study of the correlations between the free parameters and the other physical quantities that can be derived from modelling. The application programming interface (API) and functions of GalaPy are under continuous development, with planned extensions in the near future. In this first work, we introduce the project and showcase the photometric SED fitting tools already available to users. GalaPy is available on the Python Package Index (PyPI) and comes with extensive online documentation and tutorials.