arXiv subjects

Catalina Riveros-Jara

Publications and source records attributed to Catalina Riveros-Jara.

3 recordsLinked to original sources

arXiv2026

The VariableTNG project: Unveiling the physical drivers of galaxy quenching

Understanding the physical processes that regulate galaxy quenching is a key challenge in galaxy formation and evolution. The VariableTNG (VTNG) project provides a laboratory to investigate these processes, as it systematically varies eight parameters of galaxy formation while keeping the initial conditions fixed, allowing the effects of individual feedback prescriptions to be isolated. We use interpretable machine-learning techniques as a tool to identify the parameters that most strongly regulate the quenched galaxy fraction. Our goal is to quantify the relative importance of the galaxy formation and feedback parameters that regulate the quenched galaxy fraction at z=0, determine how their influence changes across stellar mass and environment. We compute the quenched galaxy fraction for 26 VTNG boxes as a function of stellar mass, black hole mass, and gas mass, considering both the total galaxy population and separate samples of central and satellite galaxies. We train Random Forest regressors to predict the variation of the quenched fraction relative to the TNG100-1 model and use SHAP values to quantify both the magnitude and direction of the influence of each parameter. Our analysis reveals that only a small subset of the VTNG parameters dominates the variance of the quenched fraction. The stellar feedback wind parameter is the primary driver at low stellar masses, while its importance gradually shifts toward AGN-related parameters at higher masses. The supernova temperature also plays an important role at both extremes of the stellar-mass range. This transition persists when the galaxy population is divided into central and satellite systems. Comparisons with observational measurements further suggest that variations in these feedback parameters may contribute to the discrepancies between the fiducial TNG100-1 model and the observed passive galaxy population.

astro-ph.GA↗

arXiv2026

Predicting galaxy bias using machine learning

Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This connection, commonly described through the galaxy bias, $b$, can be studied effectively using machine learning (ML) techniques, which offer strong predictive capabilities and can capture non-linear relationships. We aim to incorporate the linear bias parameter assigned to individual galaxies into a ML framework, quantify its dependence on various halo and environmental properties, and evaluate whether different algorithms can accurately predict this parameter and reproduce the scatter in several bias relations. We use data from the IllustrisTNG300 simulation, including the distance to different cosmic-web structures computed with DisPerSE. These data are complemented with an object-by-object estimator of the large-scale linear bias ($b_i$), providing the individual contribution of each galaxy to the bias of the entire population. Our ML framework uses three models to predict $b_i$: a Random Forest Regressor, a Neural Network and a probabilistic method (Normalizing Flows). We recover the full hierarchy of galaxy bias dependencies, showing that the most informative features are the overdensities, particularly $δ_8$, followed by the distances to cosmic-web structures and selected internal halo properties, most notably $z_{1/2}$. We also demonstrate that Normalizing Flows clearly outperform deterministic methods in predicting galaxy bias, including its joint distributions with galaxy properties, owing to their ability to capture the intrinsic variance associated with the stochastic nature of the matter-halo-galaxy connection. Our ML framework provides a foundation for future efforts to measure individual bias with upcoming spectroscopic surveys.

astro-ph.CO↗

arXiv2026

Predicted incidence of Jupiter-like planets around white dwarfs

Only a handful of gas giant planets orbiting white dwarfs are known. It remains unclear whether this paucity reflects observational challenges or the consequences of stellar evolution. We aim to carry out population synthesis of substellar objects around white dwarfs to predict the fraction and properties of white dwarfs hosting substellar companions. We generated a representative population of white-dwarf progenitors with substellar companion and used the stellar-evolution codes MESA and SSE with standard prescriptions for mass loss and stellar tides to predict the resulting population of white dwarfs and their companions. We find that the predicted fraction of white dwarfs hosting substellar companions in the Milky Way is, independent of uncertainties related to initial distributions, stellar tides, or stellar mass loss during the asymptotic giant branch, below ~3%. The occurrence rate peaks at relatively low-mass (~0.53 Msun to ~0.66 Msun) white dwarfs and relatively young (~1-6 Gyr) systems, where it exceeds 3%. The semimajor axes of the surviving companions range from 3-24 au. We estimate that ~95% of the predicted companions are gas-giant planets. Owing to the strong dependence of companion occurrence on the metallicity of the white dwarf progenitor, the assumed age-metallicity relation strongly affects the predictions. Based on recent estimates of the local age-metallicity relation, we estimate that the fraction of white dwarfs with companions close to the Sun might reach ~8%. If the planetary and brown dwarf companion distributions derived from intermediate-mass giant stars through radial velocity surveys reflect the characteristics of the true population, less than 3% of white dwarfs host substellar companions. This most likely represents an upper limit on possible detections because a significant number of companions might not be detectable with current facilities.

astro-ph.EP↗