arXiv subjects

Iván Muñoz Rodríguez

Publications and source records attributed to Iván Muñoz Rodríguez.

5 recordsLinked to original sources

arXiv2026

Data Representation Matters: Optimizing Machine Learning for X-ray Source Classification by Leveraging Spatio-Spectral Information

Distinguishing between faint extended X-ray sources is a fundamental yet challenging task in X-ray surveys. This paper presents a benchmark comparison of convolutional neural network models for classifying simulated XMM-Newton observations of AGN and galaxy clusters, focusing on the impact of data representation and interpretability of that data. We evaluate 1D, 2D, 2D+1D, and 3D CNNs trained on distinct data representations derived from simulated XMM-Newton observations of AGN and clusters: 1D (spectral), 2D (imaging) and 3D (event cube) data. We further apply 3D-GradCAM to interpret the learned features and assess the physical basis for the model's decision making to move beyond black-box classification. Our results demonstrate that increased spectral resolution enhances classifier performance using 1D and 2D information provides a strong baseline, the integration of both domains yields significant diagnostic gains. We find that a 3D CNN architecture applied directly to spatio-spectral event cubes provides the most robust performance, with superior consistency across cross-validation folds. Interpretability analysis reveals that the 3D model autonomously learns to distinguish the diffuse thermal emission of the intracluster medium from the localized, non-thermal power-law signatures of AGN. It exploits multi-dimensional correlations that are lost in lower-dimensional projections. While the performance gain over combined 2D and 1D baselines is marginal, the 3D approach demonstrates an ability to perform implicit background characterisation. By operating directly on total photon counts within event cubes, this method bypasses the need for manual background subtraction and modelling, offering a consistent, streamlined, end-to-end pipeline for automated classification of X-ray sources in large-scale catalogues.

astro-ph.IM↗

arXiv2025

The role of black hole feedback on galaxy star formation and the degeneracy with halo quenching

The interplay between the accretion of supermassive black holes (SMBHs) and the stellar mass growth of the host galaxies is still a matter of hot debate. The accretion of the SMBHs is expected to release energy under the form of AGNs. This energy is believed to impact the star formation activity and contribute to the quenching of galaxies. Here, we address this key unsolved issue with our cosmological semi-empirical model DECODE. In DECODE, we grow galaxies with their SFR linked to halo accretion rate distributions via abundance matching. SMBHs are evolved following the stellar mass growth of their host galaxies by assigning an accretion rate at each redshift from the empirical Eddington ratio distributions and duty cycles. We test the assumption that galaxies permanently quench when their central SMBHs approach the limit imposed by the observed $M_{\rm BH} - σ_\star$ relation, as a proxy of SMBH disruptive feedback. We find that simply imposing the $M_{\rm BH} - σ_\star$ condition is sufficient to generate a fraction of quenched galaxies consistent with current data, including the newest ones from Euclid. In addition, our minimal, data-driven model, also predicts SMBH scaling relations consistent in slope and normalisation with those observed, and an $M_{\rm BH} - M_\star$ relation weakly evolving with redshift. The model also naturally generates SMBH accretion rates peaking within 1 Gyr of their host SFHs. We note that all the main predictions on galaxy quenched fractions and SMBH growth histories and scaling relations are degenerate with those expected in a halo quenching model. The comprehensive data-driven model presented in this work represents an invaluable tool to investigate SMBH demography across time and environments in an accurate, physically motivated manner, ideally suited to rapidly explore the implications from large surveys, such as Euclid and Rubin-LSST.

astro-ph.GA↗

arXiv2024

Unveiling the (in)consistencies among the galaxy stellar mass function, star formation histories, satellite abundances and intracluster light from a semi-empirical perspective

In a hierarchical, dark matter-dominated Universe, stellar mass functions (SMFs), galaxy merger rates, star formation histories (SFHs), satellite abundances, and intracluster light, should all be intimately connected observables. However, the systematics affecting observations still prevent universal and uniform measurements of, for example, the SMF and the SFHs, inevitably preventing theoretical models to compare with multiple data sets robustly and simultaneously. We here present our holistic semi-empirical model DECODE (Discrete statistical sEmi-empiriCal mODEl) that converts via abundance matching dark matter merger trees into galaxy assembly histories, using different SMFs in input and predicting all other observables in output in a fully data-driven and self-consistent fashion with minimal assumptions. We find that: 1) weakly evolving or nearly constant SMFs below the knee ($M_\star \lesssim 10^{11} \, M_\odot$) are the best suited to generate star formation histories aligned with those inferred from MaNGA, SDSS, GAMA, and, more recently, JWST; 2) the evolution of satellites after infall only affects the satellite abundances and star formation histories of massive central galaxies but not their merger histories; 3) the resulting SFR-$M_\star$ relation is lower in normalization by a factor of $\sim 2$ with respect to observations, with a flattening at high masses more pronounced in the presence of mergers; 4) the latest data on intracluster light can be reproduced if mass loss from mergers is included in the models. Our findings are pivotal in acting as pathfinder to test the self-consistency of the high-quality data from, e.g., JWST and Euclid.

astro-ph.GA↗

arXiv2024

Scrutinising evidence for the triggering of Active Galactic Nuclei in the outskirts of massive galaxy clusters at $z\approx1$

Environmental effects are believed to play an important yet poorly understood role in triggering accretion events onto the supermassive black holes (SMBHs) of galaxies (Active Galactic Nuclei; AGN). Massive clusters, which represent the densest structures in the Universe, provide an excellent laboratory to isolate environmental effects and study their impact on black hole growth. In this work, we critically review observational evidence for the preferential activation of SMBHs in the outskirts of galaxy clusters. We develop a semi-empirical model under the assumption that the incidence of AGN in galaxies is independent of environment. We demonstrate that the model is broadly consistent with recent observations on the AGN halo occupation at $z=0.2$, although it may overpredict satellite AGN in massive halos at that low redshift. We then use this model to interpret the projected radial distribution of X-ray sources around high redshift ($z\approx1$) massive ($>5 \times 10^{14} \, M_\odot$) clusters, which show excess counts outside their virial radius. Such an excess naturally arises in our model as a result of sample variance. Up to 20% of the simulated projected radial distributions show excess counts similar to the observations, which are however, because of background/foreground AGN and hence, not physically associated with the cluster. Our analysis emphasises the importance of projection effects and shows that current observations of $z\approx1$ clusters remain inconclusive on the activation of SMBHs during infall.

astro-ph.GA↗

arXiv2022

Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations

This paper explores the role of small-scale environment ($<1$ Mpc) in modulating accretion events onto supermassive black holes by studying the incidence of Active Galactic Nuclei (AGN) in massive clusters of galaxies. A flexible, data-driven semi-empirical model is developed based on a minimal set of parameters and under the zero order assumption that the incidence of AGN in galaxies is independent of environment. This is used to predict how the fraction of X-ray selected AGN among galaxies in massive dark matter halos ($\gtrsim 3\times 10^{14}\,M_{\odot}$) evolves with redshift and reveal tensions with observations. At high redshift, $z\sim1.2$, the model underpredicts AGN fractions, particularly at high X-ray luminosities, $L_X(\rm 2-10\,keV) \gtrsim 10^{44}\, erg \, s^{-1}$. At low redshift, $z\sim0.2$, the model estimates fractions of moderate luminosity AGN ($L_X(\rm 2-10\,keV) \gtrsim 10^{43}\, erg \, s^{-1}$) that are a factor of $2-3$ higher than the observations. These findings reject the zero order assumption on which the semi-empirical model hinges and point to a strong and redshift-dependent influence of the small-scale environment on the growth of black holes. Cluster of galaxies appear to promote AGN activity relative to the model expectation at $z\sim1.2$ and suppress it close to the present day. These trends could be explained by the increasing gas content of galaxies toward higher redshift combined with an efficient triggering of AGN at earlier times in galaxies that fall onto clusters.

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