Ana A. Avilez Lopez | Searcharxiv

arXiv2026

Reconstructions of Horndeski Subclasses by Gaussian Processes

In this work we reconstruct two subclasses of theories from the Horndeski class, the most general scalar-tensor theories of gravity with second-order equations, according to a variety of late-universe cosmological data: model independent measurements of Hubble rate from Cosmic Chronometers, as well as Hubble rate and distance measurements from Baryonic Acoustic Oscillations. In the modified gravity family considered, the scalar field is non-minimally coupled to gravity leading to time variations in the gravitational constant at cosmological scales. The theories considered are also consistent with constraints on the propagation speed of gravitational waves. The reconstruction is carried out by using the Gaussian Processes bayesian technique either for the Brans-Dicke and the Cubic Galileon subclasses of Horndeski theory. We find that the most likely Gaussian Process reconstruction for the data favors a cosmology from the Brans-Dicke subclass over $Λ$CDM in the range $z \in (0.4, 1.7)$, and for the Cubic Galileon subclass we find that the most likely reconstruction indeed favors modifications of the scalar field to gravity with evolving dark energy density in the whole range of redshift observations.

gr-qc↗

arXiv2025

Estimating the mass of galactic components using machine learning algorithms

The estimation of the bulge and disk massses, the main baryonic components of a galaxy, can be performed using various approaches, but their implementation tend to be challenging as they often rely on strong assumptions about either the baryon dynamics or the dark matter model. In this work, we present an alternative method for predicting the masses of galactic components, including the disk, bulge, stellar and total mass, using a set of machine learning algorithms: KNN-neighbours (KNN), Linear Regression (LR), Random Forest (RF) and Neural Network (NN). The rest-frame absolute magnitudes in the ugriz-photometric system were selected as input features, and the training was performed using a sample of spiral galaxies hosting a bulge from Guo's mock catalogue \citep{Guo-Catalog} derived from the Millennium simulation. In general, all the algorithms provide good predictions for the galaxy's mass components ranging from $10^9\,M_\odot$ to $10^{11}\,M_\odot$, corresponding to the central region of the training mass domain; however, the NN give rise to the most precise predictions in comparison to other methods. Additionally, to test the performance of the NN architecture, we used a sample of observed galaxies from the SDSS survey whose mass components are known. We found that the NN can predict the luminous masses of disk-dominant galaxies within the same range of magnitudes that for the synthetic sample up to a $99\%$ level of confidence, while mass components of galaxies hosting larger bulges are well predicted up to $95\%$ level of confidence. The NN algorithm can also bring up scaling relations between masses of different components and magnitudes.

astro-ph.GA↗