arXiv subjects

Paul Goubert

Publications and source records attributed to Paul Goubert.

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

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 $\langle \sigma \rangle = 0.12 \,$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↗

arXiv2023

Galaxy quenching at the high redshift frontier: A fundamental test of cosmological models in the early universe with JWST-CEERS

We present an analysis of the quenching of star formation in massive galaxies ($M_* > 10^{9.5} M_\odot$) within the first 0.5 - 3 Gyr of the Universe's history utilizing JWST-CEERS data. We utilize a combination of advanced statistical methods to accurately constrain the intrinsic dependence of quenching in a multi-dimensional and inter-correlated parameter space. Specifically, we apply Random Forest (RF) classification, area statistics, and a partial correlation analysis to the JWST-CEERS data. First, we identify the key testable predictions from two state-of-the-art cosmological simulations (IllustrisTNG & EAGLE). Both simulations predict that quenching should be regulated by supermassive black hole mass in the early Universe. Furthermore, both simulations identify the stellar potential ($\phi_*$) as the optimal proxy for black hole mass in photometric data. In photometric observations, where we have no direct constraints on black hole masses, we find that the stellar potential is the most predictive parameter of massive galaxy quenching at all epochs from $z = 0 - 8$, exactly as predicted by simulations for this sample. The stellar potential outperforms stellar mass, galaxy size, galaxy density, and S\'ersic index as a predictor of quiescence at all epochs probed in JWST-CEERS. Collectively, these results strongly imply a stable quenching mechanism operating throughout cosmic history, which is closely connected to the central gravitational potential in galaxies. This connection is explained in cosmological models via massive black holes forming and growing in deep potential wells, and subsequently quenching galaxies through a mix of ejective and preventative active galactic nucleus (AGN) feedback.

astro-ph.GA↗