arXiv subjects

V. Busillo

Publications and source records attributed to V. Busillo.

8 recordsLinked to original sources

arXiv2026

CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations IV. Testing warm dark matter cosmologies with galaxy scaling relations: A joint simulation-observation study using DREAMS simulations

Small-scale discrepancies in the standard Lambda cold dark matter paradigm have motivated the exploration of alternative dark matter (DM) models, such as warm dark matter (WDM). We investigate the constraining power of galaxy scaling relations on cosmological, astrophysical, and WDM parameters through a joint analysis of hydrodynamic simulations and observational data. Our study is based on the DREAMS project and combines large-volume uniform-box simulations with high-resolution Milky Way zoom-in runs in a $\Lambda$WDM cosmology. To ensure consistency between the different simulation sets, we apply calibrations to account for resolution effects, allowing us to exploit the complementary strengths of the two suites. We compare simulated relations, including stellar size, DM mass and fraction within the stellar half-mass radius, and the total-to-stellar mass ratio, with two complementary galaxy samples: the SPARC catalog of nearby spirals and the LVDB catalog of dwarf galaxies in the Local Volume. Using a bootstrap-based fitting procedure, we show that key cosmological parameters ($\Omega_m$, $\sigma_8$) and supernova feedback strength can be recovered with good accuracy, particularly from the uniform-box simulations. While the WDM particle mass remains unconstrained, the zoom-in simulations reveal subtle WDM-induced trends at low stellar masses in both the DM mass and total-to-stellar mass ratio. We also find that the galaxy stellar mass function exhibits a measurable dependence on the WDM particle mass below log10(M_*/Msun) <~ 8, which appears separable from the impact of feedback, suggesting it as a promising complementary probe. Our results highlight the importance of combining multi-resolution simulations with diverse observational datasets to jointly constrain baryonic processes and DM properties.

astro-ph.GA↗

arXiv2025

Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue

We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg$^2$). In the initial 0.45\% of Euclid's surveys, we double the total number of known lens candidates with space-based imaging. Our catalogue includes 250 grade A candidates, the vast majority of which (243) were previously unpublished. Euclid's resolution reveals rare lens configurations of scientific value including double-source-plane lenses, edge-on lenses, complete Einstein rings, and quadruply-imaged lenses. We resolve lenses with small Einstein radii ($\theta_{\rm E} < 1''$) in large numbers for the first time. These lenses are found through an initial sweep by deep learning models, followed by Space Warps citizen scientist inspection, expert vetting, and system-by-system modelling. Our search approach scales straightforwardly to Euclid Data Release 1 and, without changes, would yield approximately 7000 high-confidence (grade A or B) lens candidates by late 2026. Further extrapolating to the complete Euclid Wide Survey implies a likely yield of over 100000 high-confidence candidates, transforming strong lensing science.

astro-ph.GA↗

arXiv2025

Euclid Quick Data Release (Q1). LEMON -- Lens Modelling with Neural networks. Automated and fast modelling of Euclid gravitational lenses with a singular isothermal ellipsoid mass profile

The Euclid mission aims to survey around 14000 deg^{2} of extragalactic sky, providing around 10^{5} gravitational lens images. Modelling of gravitational lenses is fundamental to estimate the total mass of the lens galaxy, along with its dark matter content. Traditional modelling of gravitational lenses is computationally intensive and requires manual input. In this paper, we use a Bayesian neural network, LEns MOdelling with Neural networks (LEMON), to model Euclid gravitational lenses with a singular isothermal ellipsoid mass profile. Our method estimates key lens mass profile parameters, such as the Einstein radius, while also predicting the light parameters of foreground galaxies and their uncertainties. We validate LEMON's performance on both mock Euclid datasets, real lenses observed with Hubble Space Telescope (HST), and real Euclid lenses, demonstrating the ability of LEMON to predict parameters of both simulated and real lenses. Results show promising accuracy and reliability in predicting the Einstein radius, mass and light ellipticities, effective radius, S\'ersic index, lens magnitude, and unlensed source position for simulated lens galaxies. The application to real data, including the latest Quick Release 1 strong lens candidates, provides encouraging results in the recovery of the parameters for real lenses. We also verified that LEMON has the potential to accelerate traditional modelling methods, by giving to the classical optimiser the LEMON predictions as starting points, resulting in a speed-up of up to 26 times the original time needed to model a sample of gravitational lenses, a result that would be impossible with randomly initialised guesses. This work represents a significant step towards efficient, automated gravitational lens modelling, which is crucial for handling the large data volumes expected from Euclid.

astro-ph.CO↗

arXiv2025

CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations III. The physics behind the emergence of the golden mass scale

We investigate the origin and evolution of the "golden mass" (halo mass $\sim10^{12} \, \rm M_{\odot}$, stellar mass $\sim5 \times 10^{10} \, \rm M_{\odot}$), linked to peak star formation efficiency, using \textsc{camels} simulations based on IllustrisTNG. Exploring a range of SN/AGN feedback strengths and cosmologies ($\Omega_{\rm m}, \sigma_8$), we find a U-shaped relation between dark-to-stellar mass ratio and stellar mass, with a minimum at the golden mass, in line with observations. Cosmology affects the normalization of this relation, while feedback shapes its form and the emergence of the golden mass. Stronger SN feedback lowers its value; AGN feedback, especially radiative efficiency, alters the high-mass slope and shifts the golden mass. The golden mass appears earlier with stronger feedback, which quenches star formation more rapidly. Passive galaxies retain the U-shape; star-forming ones show decreasing dark matter fraction with stellar mass, with possible reversal at low redshift. Global stellar fractions also exhibit a U-shaped trend: in passive galaxies, the golden mass shifts to lower masses or vanishes; in star-forming ones, it emerges only at low redshift. Feedback governs the golden mass up to $z \sim 1.5-2$, with a secondary role from cold streams and virial shocks. We speculate that at $z \gtrsim 1.5-2$, a single stream-regulated scale governs galaxy growth, which later bifurcates into two: a low-mass scale tied to gas richness and a higher-mass golden mass regulating efficiency and quenching. (abridged)

astro-ph.GA↗

arXiv2025

Euclid: Finding strong gravitational lenses in the Early Release Observations using convolutional neural networks

The Early Release Observations (ERO) from Euclid have detected several new galaxy-galaxy strong gravitational lenses, with the all-sky survey expected to find 170,000 new systems, greatly enhancing studies of dark matter, dark energy, and constraints on the cosmological parameters. As a first step, visual inspection of all galaxies in one of the ERO fields (Perseus) was carried out to identify candidate strong lensing systems and compared to the predictions from Convolutional Neural Networks (CNNs). However, the entire ERO data set is too large for expert visual inspection. In this paper, we therefore extend the CNN analysis to the whole ERO data set, using different CNN architectures and methodologies. Using five CNN architectures, we identified 8,469 strong gravitational lens candidates from IE-band cutouts of 13 Euclid ERO fields, narrowing them to 97 through visual inspection, including 14 grade A and 31 grade B candidates. We present the spectroscopic confirmation of a strong gravitational lensing candidate, EUCLJ081705.61+702348.8. The foreground lensing galaxy, an early-type system at redshift z = 0.335, and the background source, a star-forming galaxy at redshift z = 1.475 with [O II] emission, are both identified. Lens modeling using the Euclid strong lens modeling pipeline reveals two distinct arcs in a lensing configuration, with an Einstein radius of 1.18 \pm 0.03 arcseconds, confirming the lensing nature of the system. These findings highlight the importance of a broad CNN search to efficiently reduce candidates, followed by visual inspection to eliminate false positives and achieve a high-purity sample of strong lenses in Euclid.

astro-ph.GA↗

arXiv2024

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent.

astro-ph.IM↗

arXiv2024

Euclid: The Early Release Observations Lens Search Experiment

We investigated the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we performed a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid Early Release Observations data towards the Perseus cluster using both the high-resolution $I_{\scriptscriptstyle\rm E}$ band and the lower-resolution $Y_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$, $H_{\scriptscriptstyle\rm E}$ bands. Each extended source brighter than magnitude 23 in $I_{\scriptscriptstyle\rm E}$ was inspected by 41 expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We found $3$ grade A and $13$ grade B candidates. We assessed the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling, and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small-Einstein-radius systems. Whilst it is implausible to visually inspect the full Euclid dataset, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses.

astro-ph.GA↗

arXiv2023

AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from angular power spectrum and correlation function

We study the tomographic clustering properties of the photometric cluster catalogue derived from the Third Data Release of the Kilo Degree Survey, focusing on the angular correlation function and its spherical harmonic counterpart, the angular power spectrum. We measure the angular correlation function and power spectrum from a sample of 5162 clusters, with an intrinsic richness $\lambda^*\geq 15$, in the photometric redshift range $z\in [0.1, 0.6]$, comparing our measurements with theoretical models, in the framework of the $\Lambda$-Cold Dark Matter cosmology. We perform a Monte Carlo Markov Chain analysis to constrain the cosmological parameters $\Omega_{\mathrm{m}}$, $\sigma_8$ and the structure growth parameter $S_8\equiv\sigma_8 \sqrt{\Omega_{\mathrm{m}}/0.3}$. We adopt Gaussian priors on the parameters of the mass-richness relation, based on the posterior distributions derived from a previous joint analysis of cluster counts and weak lensing mass measurements carried out with the same catalogue. From the angular correlation function, we obtain $\Omega_{\mathrm{m}}=0.32^{+0.05}_{-0.04}$, $\sigma_8=0.77^{+0.13}_{-0.09}$ and $S_8=0.80^{+0.08}_{-0.06}$, in agreement, within $1\sigma$, with 3D clustering result based on the same cluster sample and with existing complementary studies on other datasets. For the angular power spectrum, we derive statistically consistent results, in particular $\Omega_{\mathrm{m}}=0.24^{+0.05}_{-0.04}$ and $S_8=0.93^{+0.11}_{-0.12}$, while the constraint on $\sigma_8$ alone is weaker with respect to the one provided by the angular correlation function, $\sigma_8=1.01^{+0.25}_{-0.17}$. Our results show that the 2D clustering from photometric cluster surveys can provide competitive cosmological constraints with respect to the full 3D clustering statistics, and can be successfully applied to ongoing and forthcoming spectro/photometric surveys.

astro-ph.CO↗