<link>https://tommasoronconi.github.io/</link></image><item><title>The galaxy–halo connection</h1> <article> <h1>The galaxy–halo connection</h1> <p>Wed, 29 Jul 2026 00:00:00 +0000</p> <p>A central axis of my work is the development of empirical models linking dark matter haloes to galaxies, in order to generate realistic mock galaxy catalogues for <strong>SKAO</strong> and <strong>Euclid</strong>.</p> <p>As a major outcome of my Ph.D. thesis I created <strong>SCAMPy</strong>, a hybrid C++/Python library for <em>painting</em> observed galaxy populations on top of the dark matter halo/sub-halo hierarchy of N-body simulations. It implements the Sub-halo Clustering and Abundance Matching scheme: a Halo Occupation Distribution prescription first selects which sub-haloes host galaxies, then sub-halo abundance matching assigns observable properties to each object. The method requires only the observed distribution of the target property — a luminosity function, say — and the clustering statistics of the target population. The result is a computationally efficient, fully data-driven way to produce mocks that reproduce both the 1- and 2-point statistics of any observed sample, from high-redshift Lyman-break galaxies to low-redshift radio sources. SCAMPy has since become a core ingredient in the simulation pipelines used for SKAO preparatory science.</p> <p>During a research stay at SKAO Headquarters I also contributed to <strong>T-RECS</strong>, a tool for simulating radio sources in continuum and HI line, now widely used in the radio-cosmology community for survey design and theoretical modelling.</p> <p>This line of work recently culminated in a comprehensive framework for constructing full-sky empirical mock catalogues of the radio sky: a modular pipeline combining a simulated dark-matter light-cone, built from the high-resolution DEMNUni N-body simulations, with empirically sampled galaxy populations generated by T-RECS and assigned to haloes via an extended version of SCAMPy. The resulting catalogues cover the full 4π steradians down to redshift <em>z</em> = 5 and place multiple radio populations on a common light-cone: continuum-emitting AGN and star-forming galaxies alongside HI line-emitting sources. No single existing approach had previously combined full-sky coverage, multiple radio populations, empirical flexibility and modularity.</p> <p>I currently lead a European collaboration integrating these pipelines to enable joint radio continuum and line studies. In this context I coordinated the <strong>HI Simulations chapter</strong> for the next edition of <em>Advancing Astrophysics with the SKA</em> (AASKA II), bringing together researchers from four continents to compile a comprehensive description and comparison of the simulations available for studying post-reionization HI.</p> <p><strong>Currently:</strong> extending the framework to incorporate multi-wavelength counterparts, connecting radio populations to their optical, infrared and sub-mm emission for cross-survey analyses combining SKAO with Euclid and other facilities. I am also exploring reinforcement-learning approaches to extend the empirical models underlying the galaxy–halo connection.</p> <hr> <p><em>Key papers:</em> · · </p> <p><em>Software:</em> · </p> </article> <article> <h1>GalaPy</h1> <p>Wed, 01 May 2024 00:00:00 +0000</p> <p><strong>Role:</strong> lead author & maintainer. <strong>Stack:</strong> C++ compute core exposed through a Python API; hybrid parallelisation (vectorised array programming, shared-memory concurrency, distributed-memory message passing).</p> <p>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 <strong>1000 SEDs per second on a single core</strong> — the fastest SED-generation tool of its kind.</p> <p>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.</p> <p>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 .</p> <p>Presented in <em>Astronomy & Astrophysics</em> 685, A161 (2024) — see — with the computational design detailed in <em>Astronomy and Computing</em> 55, 101079 (2026): .</p> </article> <article> <h1>Galaxy formation and evolution</h1> <p>Wed, 29 Jul 2026 00:00:00 +0000</p> <p>A second major axis is galaxy formation and evolution across cosmic time. With collaborators at SISSA I have analysed the spectral properties of high-redshift galaxies using theoretical models of star formation and dust processes.</p> <p>This led to <strong>GalaPy</strong>, a public code that simulates the panchromatic emission of galaxies from X-rays to radio, offering full SED modelling, Bayesian inference, and a computationally efficient implementation. It is the fastest SED-generation tool of its kind, with a peak performance of almost <strong>1000 SEDs per second on a single core</strong>, thanks to a hybrid C++/Python architecture that generates models on the fly rather than relying on templates.</p> <p>Since its release GalaPy has gained users across Europe, Asia and South America, and has been adopted for studies ranging from nearby resolved galaxies to JWST high-redshift candidates. In a recent paper I presented the computational design of the code in detail: its object-oriented architecture, a hybrid parallelisation strategy combining vectorised array programming, shared-memory concurrency and distributed-memory message passing, and the optimisations that keep it portable with minimal memory overhead. That work also introduced two new capabilities — parallel sampling for Bayesian inference, and a fully analytical panchromatic AGN component.</p> <p><strong>Currently:</strong> GalaPy is developing along two directions. I am co-supervising a Ph.D. student implementing panchromatic line emission in GalaPy SEDs, covering both the physics of emission lines and the sampling strategies needed for efficient inference. Meanwhile growing adoption for JWST and ALMA data is driving further computational optimisation and validated spectroscopic fitting beyond the current photometric mode.</p> <hr> <p><em>Key papers:</em> · </p> <p><em>Software:</em> </p> </article> <article> <h1>SCAMPy</h1> <p>Sat, 01 Aug 2020 00:00:00 +0000</p> <p><strong>Role:</strong> author & maintainer. <strong>Stack:</strong> C++ core (object-oriented, polymorphic) wrapped in Python.</p> <p>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.</p> <p>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.</p> <p>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 .</p> <p>Presented in <em>Monthly Notices of the Royal Astronomical Society</em> 498, 2095 (2020) — see . Also indexed in the Astrophysics Source Code Library as <code>ascl:2002.006</code>.</p> </article> <article> <h1>Cosmic voids</h1> <p>Wed, 29 Jul 2026 00:00:00 +0000</p> <p>A major research theme has been the study of <strong>cosmic voids</strong> — vast, under-dense regions occupying most of the volume of the Universe. When I began working on my Master’s thesis, the community had theoretically demonstrated that the statistical properties of these structures could be exploited to constrain dark energy and test theories of gravity, but the theoretical models describing the void size distribution consistently failed to predict both simulated and observed data.</p> <p>My work has paved the way for the cosmological exploitation of the <strong>void size function</strong> when voids are identified in any distribution of tracers, including real data catalogues. In Ronconi & Marulli (2017) we presented an algorithm that redefines void ridges and, consequently, their radii; I implemented it inside <strong>CosmoBolognaLib</strong>, a large set of open-source numerical libraries for cosmological calculations.</p> <p>With this tool we then demonstrated that, as long as our specifications are accounted for, the size function is a viable approach for studying cosmology with cosmic voids. The result was further validated by its adoption as a fundamental tool in Key Projects within the Euclid Collaboration, used to forecast the cosmological constraining power of void statistics.</p> <p><strong>Currently:</strong> I am working with members of the Euclid Collaboration on void statistics in mock galaxy catalogues, and investigating a new theoretical model of the void distribution based on stochastic differential equations, together with Andrea Lapi at SISSA. A Ph.D. student has recently been selected to pursue this direction over the next three years under our supervision.</p> <hr> <p><em>Key papers:</em> · · · </p> <p><em>Software:</em> CosmoBolognaLib </p> </article> <article> <h1>T-RECS</h1> <p>Mon, 01 May 2023 00:00:00 +0000</p> <p><strong>Role:</strong> contributor — HI emission extension. <strong>Stack:</strong> Fortran executables with Python tooling for catalogue cross-matching. <strong>Lead author & maintainer:</strong> Anna Bonaldi (SKAO).</p> <p>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.</p> <p>During a research stay at SKAO Headquarters in Manchester I contributed to the second generation of the code, which extends T-RECS to <strong>HI line emission</strong>. 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.</p> <p>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.</p> <p>The HI extension is presented in <em>Monthly Notices of the Royal Astronomical Society</em> 524, 993 (2023) — see .</p> </article> <article> <h1>CosmoBolognaLib</h1> <p>Wed, 01 Nov 2017 00:00:00 +0000</p> <p><strong>Role:</strong> contributor — void size function modelling and void-catalogue cleaning. <strong>Stack:</strong> C++ libraries with a SWIG-generated Python wrapper. <strong>Author & maintainer:</strong> Federico Marulli (Università di Bologna).</p> <p>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.</p> <p>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).</p> <p>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.</p> <p>The void tools are presented in <em>Astronomy & Astrophysics</em> 607, A24 (2017) — see . The library itself is described in Marulli, Veropalumbo & Moresco (2016).</p> </article> <article> <h1>Contacts</h1> <p>Thu, 30 Jul 2026 00:00:00 +0000</p> </article> <article> <h1>Page under construction</h1> <p>Tue, 28 Jul 2026 00:00:00 +0000</p> <figure> </figure> <p>This section is not online yet — it is being built. In the meantime, you can browse my or .</p> </article> <article> <h1>Cosmology from Synergies Between SKAO Surveys and Gravitational Wave Observations</h1> <p>Mon, 01 Jun 2026 00:00:00 +0000</p> </article> <article> <h1>HI Simulations for Cosmology with the SKA Observatory</h1> <p>Mon, 01 Jun 2026 00:00:00 +0000</p> </article> <article> <h1>Radio sirens: inferring $H_0$ with binary black holes and neutral hydrogen in the era of the Einstein Telescope and the SKA Observatory</h1> <p>Fri, 01 May 2026 00:00:00 +0000</p> </article> <article> <h1>GalaPy—Implementation strategies of the spectral modelling tool for galaxies in Python</h1> <p>Wed, 01 Apr 2026 00:00:00 +0000</p> </article> <article> <h1>Ultrahigh-redshift or closer-by, dust-obscured galaxies?: Deciphering the nature of faint, previously missed F200W dropouts in CEERS</h1> <p>Wed, 01 Apr 2026 00:00:00 +0000</p> </article> <article> <h1>Painting a full radio sky -- Empirical mock catalogues with multiple source populations for future radio surveys</h1> <p>Sun, 01 Mar 2026 00:00:00 +0000</p> </article> <article> <h1>Spatially resolved spectrophotometric SED modeling of NGC 253's central molecular zone: I. Star formation in extragalactic giant molecular clouds</h1> <p>Tue, 01 Jul 2025 00:00:00 +0000</p> </article> <article> <h1>GalaPy: A highly optimised C$_++$/Python spectral modelling tool for galaxies. I. Library presentation and photometric fitting</h1> <p>Wed, 01 May 2024 00:00:00 +0000</p> </article> <article> <h1>Little Ado about Everything: ensuremathηCDM, a Cosmological Model with Fluctuation-driven Acceleration at Late Times</h1> <p>Fri, 01 Dec 2023 00:00:00 +0000</p> </article> <article> <h1>Experience</h1> <p>Tue, 24 Oct 2023 00:00:00 +0000</p> </article> <article> <h1>The tiered radio extragalactic continuum (T-RECS) simulation II: H I emission and continuum-H I cross-correlation</h1> <p>Fri, 01 Sep 2023 00:00:00 +0000</p> </article> <article> <h1>The Way of Water: ALMA Resolves H$_2$O Emission Lines in a Strongly Lensed Dusty Star-forming Galaxy at z ensuremath∼ 3.1</h1> <p>Sat, 01 Jul 2023 00:00:00 +0000</p> </article> <article> <h1>ALMA Resolves the First Strongly Lensed Optical/Near-IR-dark Galaxy</h1> <p>Wed, 01 Feb 2023 00:00:00 +0000</p> </article> <article> <h1>A Stochastic Theory of the Hierarchical Clustering. III. The Nonuniversality and Nonstationarity of the Halo Mass Function</h1> <p>Thu, 01 Dec 2022 00:00:00 +0000</p> </article> <article> <h1>Astroparticle Constraints from the Cosmic Star Formation Rate Density at High Redshift: Current Status and Forecasts for JWST</h1> <p>Tue, 01 Nov 2022 00:00:00 +0000</p> </article> <article> <h1>Euclid: Cosmological forecasts from the void size function</h1> <p>Tue, 01 Nov 2022 00:00:00 +0000</p> </article> <article> <h1>Astroparticle Constraints from Cosmic Reionization and Primordial Galaxy Formation</h1> <p>Thu, 01 Sep 2022 00:00:00 +0000</p> </article> <article> <h1>Cosmology with the submillimetre galaxies magnification bias. Tomographic analysis</h1> <p>Wed, 01 Dec 2021 00:00:00 +0000</p> </article> <article> <h1>An ALMA view of 11 dusty star-forming galaxies at the peak of cosmic star formation history</h1> <p>Mon, 01 Nov 2021 00:00:00 +0000</p> </article> <article> <h1>Intensity and anisotropies of the stochastic gravitational wave background from merging compact binaries in galaxies</h1> <p>Mon, 01 Nov 2021 00:00:00 +0000</p> </article> <article> <h1>VizieR Online Data Catalog: Tomography with Magnification Bias (Bonavera+, 2021)</h1> <p>Wed, 01 Sep 2021 00:00:00 +0000</p> </article> <article> <h1>Unveiling the nature of 11 dusty star-forming galaxies at the peak of cosmic star formation history</h1> <p>Tue, 01 Jun 2021 00:00:00 +0000</p> </article> <article> <h1>From cosmic voids to collapsed structures: HPC methods for Astrophysics and Cosmology</h1> <p>Thu, 01 Oct 2020 00:00:00 +0000</p> </article> <article> <h1>SCAMPY - A sub-halo clustering and abundance matching based PYTHON interface for painting galaxies on the dark matter halo/sub-halo hierarchy</h1> <p>Thu, 01 Oct 2020 00:00:00 +0000</p> </article> <article> <h1>Cosmology with the submillimetre galaxies magnification bias: Proof of concept</h1> <p>Wed, 01 Jul 2020 00:00:00 +0000</p> </article> <article> <h1>Cosmology with the submillimetre galaxies magnification bias: Proof of concept</h1> <p>Wed, 01 Jul 2020 00:00:00 +0000</p> </article> <article> <h1>ScamPy: Sub-halo Clustering and Abundance Matching Python interface</h1> <p>Sat, 01 Feb 2020 00:00:00 +0000</p> </article> <article> <h1>Cosmic voids uncovered - first-order statistics of depressions in the biased density field</h1> <p>Tue, 01 Oct 2019 00:00:00 +0000</p> </article> <article> <h1>Cosmological exploitation of the size function of cosmic voids identified in the distribution of biased tracers</h1> <p>Sun, 01 Sep 2019 00:00:00 +0000</p> </article> <article> <h1>Cosmological exploitation of cosmic void statistics. New numerical tools in the CosmoBolognaLib to extract cosmological constraints from the void size function</h1> <p>Sun, 01 Oct 2017 00:00:00 +0000</p> </article> </main></body></html>