arXiv subjects

Thomas Pinto Franco

Publications and source records attributed to Thomas Pinto Franco.

3 recordsLinked to original sources

arXiv2026

Beyond the Merger-Quasar-Quench Paradigm I: Mergers are neither necessary nor sufficient to quench central galaxies in IllustrisTNG

The cessation of star formation in galaxies, known as 'quenching', is a complex, multi-scale process which has been theorized to be linked to galaxy mergers. In this paper, we investigate the potential role of mergers in quenching galaxies in the IllustrisTNG cosmological hydrodynamical simulation. We track the evolution of over 11,000 central galaxies in the simulation with stellar mass $M_\star \ge 10^9 M_\odot$ at $z = 0$ throughout the entirety of cosmic history. We compare their star formation and merger histories to test whether mergers are necessary or sufficient for inducing quenching in the simulation. Only a very small fraction of mergers (about 3 per cent of major mergers and about 12 per cent of all mergers) lead to quenching within 1 Gyr, indicating that mergers are not sufficient by themselves to cause quenching. Furthermore, the vast majority of quenching events are not preceded by a merger within 1 Gyr. Once random coincidences are accounted for and a stellar mass-matched control sample is applied, no merger excess is observed. Hence, mergers are clearly not necessary for quenching to occur in the simulation. Finally, we perform a series of random forest classification and regression analyses to assess the integrated role of mergers in galaxy quenching and supermassive black hole growth in IllustrisTNG. We determine that secular processes dominate the growth of supermassive black holes and the quenching of central galaxies in this simulation, in stark contrast to prior theoretical expectations from idealized hydrodynamical simulations.

astro-ph.GA↗

arXiv2025

Environmental vs. intrinsic quenching at cosmic noon: Predictions from cosmological hydrodynamical simulations for VLT-MOONRISE

We present an investigation into the quenching of simulated galaxies across cosmic time, honing in on the role played by both intrinsic and environmental mechanisms at different epochs. In anticipation of VLT-MOONRISE, the first wide-field spectroscopic galaxy survey to target cosmic noon, this work provides clear predictions to compare to the future observations. We investigate the quenching of centrals, high-mass satellites, and low-mass satellites from two cosmological hydrodynamical simulations: IllustrisTNG and EAGLE. Satellites are split according to bespoke mass thresholds, designed to separate environmental and intrinsic quenching mechanisms. To determine the best parameter for predicting quiescence, we apply a Random Forest classification analysis for each galaxy class at each epoch. The Random Forest classification determines supermassive black hole mass as the best predictor of quiescence in centrals and high-mass satellites. Alternatively, the quenching of low-mass satellites is best predicted by group halo mass, at all epochs. Additionally, we investigate the evolution in the dependence of the quenched fraction with various parameters, revealing a more complex picture. There is strong evidence for the rejuvenation of star formation from z = 2 to z = 0 in EAGLE, but not in IllustrisTNG. The starkest discrepancy between simulations rests in the mass threshold analysis. While IllustrisTNG predicts the existence of environmentally quenched satellites visible within the survey limits of MOONRISE, EAGLE does not. Hence, MOONRISE will provide critical data that is needed to evaluate current models, and constrain future models, of quenching processes.

astro-ph.GA↗

arXiv2025

Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing

We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters and yields full probability distribution functions for the output halo masses. To achieve this, we train a series of Random Forest (RF) regression models on the IllustrisTNG and EAGLE simulations at z=0-3, which provide model-dependent mappings from luminous tracers to dark matter halo properties. We incorporate the individual regression models into a virial group-finding algorithm (DfL), which outputs halo properties for observational-like input data. We test the method at z=0-2 for both the EAGLE and IllustrisTNG models, as well as in a cross-validation mode. We demonstrate that known halo masses can be recovered with a mean systematic bias of $\langle b \rangle = \pm 0.10\,$dex (resulting from simulation choice), a mean statistical uncertainty of $\langle σ\rangle = 0.12 \,$dex across epochs, and a central - (core) satellite classification accuracy of 96%. We establish that this approach yields superior halo mass recovery to standard abundance matching applied to groups identified through a friends-of-friends algorithm. Additionally, we compare the outputs of DfL to observational constraints on the $M_* - M_{\rm Halo}$ relation from strong gravitational lensing at $z \sim 0$, demonstrating the promise of this novel approach. Finally, we systematically quantify how DfL performs on observational-like input data with varying stellar mass uncertainty and spectroscopic incompleteness, enabling robust error calibration.

astro-ph.CO↗