Nicola R. Napolitano

The Fundamental Plane (FP) of Early-Type Galaxies (ETGs) encapsulates a tight correlation among their structural and dynamical properties and provides an important benchmark for galaxy formation models. However, cosmological hydrodynamical simulations have historically struggled to reproduce the observed FP tilt, with discrepancies often attributed to flawed feedback physics or insufficient resolution. Using the IllustrisTNG100-1 simulation, we show that adopting observationally motivated measurements, including Sérsic-derived photometric parameters and dynamically inferred velocity dispersions designed to minimise softening-length effects, substantially reduces the discrepancy between simulated and observed FPs. We further explore the impact of non-universal, mass-dependent Initial Mass Function (IMF) variations through forward modelling of their effects on galaxy structural and dynamical quantities. In particular, within the adopted r-band framework, bottom-heavy IMF variations bring the mean FP coefficients into close agreement with observational constraints for both direct and orthogonal fits, while the simulated FP remains thinner than the observed relation. Our results suggest that a significant fraction of the long-standing FP tension arises from how galaxy observables are extracted and interpreted in simulations, although residual discrepancies may still reflect limitations in the underlying baryonic physics. These findings highlight the importance of observational realism and IMF variations for interpreting galaxy scaling relations and for improving the predictive power of hydrodynamical simulations of ETG formation.

NGC 1399, the bright central galaxy of the Fornax cluster, contains an extensive population of globular clusters (GCs) extending beyond 200 kpc into its outer halo, approximately 6.5 effective radii (R _e ). We conducted dynamical mass modelling of NGC 1399 out to 5 R _e (∼150 kpc) using the most comprehensive radial velocity catalogue of GCs to date, which includes data from the Fornax cluster VLT Spectroscopic Survey (FVSS) and previous velocity measurements from the literature. Applying spherical Jeans equations, we performed dispersion-kurtosis modelling of NGC 1399’s GC kinematics to derive its mass profile and the orbital anisotropy of its GCs. We also investigated the impact of including intra-cluster GCs in the mass modelling, which were selected based on spatial segregation and a velocity σ -clipping method. Our findings indicate that both cusp-like (NFW) and core-like (Burkert) halos can reproduce the observed kinematics of GCs. Including the intra-cluster GCs in the mass modelling yields broadly consistent mass profiles for the two halo types, with the full GC sample giving a virial mass of log M _vir = 13.81 ± 0.09 M _⊙ for the NFW halo, slightly higher than the value inferred for the Burkert halo. Regardless of the dark matter halo profile adopted, we observe that the intra-cluster GCs exhibit radial anisotropy in the outskirts, especially the blue, metal-poor GCs. These results suggest that GCs in NGC 1399’s outer halo are influenced by an additional halo component associated with the galaxy cluster potential. The radial anisotropy observed in these outer-halo GCs provides strong dynamical evidence that they were accreted from external sources, which is consistent with prior photometric and spectroscopic studies of GCs. This study highlights the importance of including intra-cluster GCs in dynamical mass modelling to fully account for the contribution of dark matter to the cluster gravitational potential.

Galaxy morphology classification is fundamental to understanding galaxy formation and evolution. The advent of large-scale sky surveys has produced an unprecedented volume of galaxy images, making traditional manual classification impractical. Although supervised deep learning can achieve high accuracy, it requires large labeled datasets that are time-consuming to construct. In contrast, unsupervised methods often show limited classification performance. To address this limitation, we propose a label-efficient self-supervised learning framework for galaxy morphology classification. Our method first learns robust morphological representations from 305,583 unlabeled KiDS galaxy images through contrastive learning, and then trains a classifier using only 5000 human-labeled images. The classifier separates galaxies into five categories: elliptical, spiral, lenticular-disk, irregular, and “other.” Using a ResNet-50 model with a crop size of 64 × 64 pixels, our approach achieves an overall test accuracy of up to 91.0% (90.5% ± 0.2% on average) on the human-classified catalog. The corresponding F1 scores for elliptical, spiral, irregular, lenticular-disk, and “other” galaxies are 0.96, 0.86, 0.86, 0.95, and 0.92, respectively. We apply this pipeline to the Kilo-Degree Survey Data Release 5 and produce a publicly available morphology catalog of 310,583 galaxies. This is the first morphology catalog for KiDS galaxies and provides a valuable resource for future studies of galaxy evolution. Our results show that self-supervised learning can substantially reduce the need for manual labels while maintaining high classification accuracy, making it a promising and scalable approach for automated galaxy morphology classification in the era of large-scale surveys.

We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy–galaxy strong lensing (GGSL) images. By integrating synthetic spectral energy distributions (SEDs), the pipeline accurately models the redshift-dependent photometric properties of lens and source galaxies, ensuring physical consistency across multi-band observations. The framework incorporates key observational parameters, including point spread functions (PSFs), magnitude limits, and zero points, to replicate specific survey conditions and enable robust cross-survey joint analyses. As an application, we simulate multi-band images for the Kilo-Degree Survey (KiDS), Legacy Survey of Space and Time (LSST), and Euclid using identical lens model parameters and employ a deep learning network to evaluate image deblending performance. In particular, the simulation leverages PyTorch to ensure full auto-differentiability and GPU acceleration, making it a highly efficient tool for advanced deep learning algorithms that require gradient-based optimization beyond standard model training. Our framework achieves a speedup of approximately O ( 1 0 3 ) over traditional CPU-based pipelines, demonstrating, for the first time, the feasibility of joint gradient-based lens modeling across next-generation surveys.

We present a study of dwarf galaxy candidates in the deepest optical imaging obtained to date of the field surrounding the nearby galaxies NGC 5068 (5.2 Mpc) and NGC 5084/NGC 5087 (25–30 Mpc). The latter two are located in the peripheries of the Virgo Supercluster. This field, covering ∼2.6 deg ² , was observed as part of the multi-band, wide-field, and very deep data from VST Survey of Mass Assembly and Structural Hierarchy (VST-SMASH), a distance-limited programme ( D < 11 Mpc) that reaches g - and r -band surface brightness depths of µ ∼ 30 mag arcsec ⁻² within a box of 10×10 arcsec ² at 1 σ . Using a two-step visual inspection procedure, we identified 47 dwarf galaxy candidates and performed a surface photometry study of the sample, along with a fitting procedure with 1D Sérsic model on their profiles. Only four galaxies had previously been reported in the literature, augmenting the number of dwarfs discovered in these regions by one order of magnitude. The colours (median g − r = 0.57 mag and r − i = 0.24 mag) and structural properties of the dwarf candidates are consistent with the literature, as are their scaling relations with effective radius, Sérsic index ( n < 2), and absolute magnitude. We also investigated their central colour gradients, which exhibit significant scatter. We discuss them within the broader context of galaxy formation and also analyse the spatial distribution of these dwarf candidates relative to potential host galaxies. Considering the projected separation, we identified reasonable associations with NGC 5084, NGC 5087, and NGC 5068 as likely hosts for a significant fraction of the sample. Several candidates are at physically credible distances from NGC 5068, despite what their offset size–luminosity relation alone might suggest. Future spectroscopic and deeper imaging follow-ups are required to determine distances and velocities, enabling a robust association with hosts, studies of satellite distributions and counts, and comparisons with cosmological expectations for planes in the context of satellites and dark matter models.

Context. Many lensing images are often obscured by foreground light from the central galaxies, making them challenging to detect. Aims. To address the limitations of previous lens search efforts, particularly for samples with smaller R _E or faint lensed images, we developed a composite convolutional neural network framework that utilizes both U-Net and ResNet architectures for feature extraction and classification. Methods. We propose a hybrid search method that combines U-Net and ResNet architectures to enhance the detection of the foreground galaxy-obscured lenses. Our approach consists of two main stages: first, the U-Net model separates the foreground galaxy light from potential lensing signals, creating residual images that highlight the lensing features. Next, the ResNet module performs binary classification on these residual images to detect lensing signals. Results. We evaluated the hybrid search method with real observational data to demonstrate its effectiveness, achieving a recall of 71.5% and a 4.5% false positive rate at a confidence threshold of 0.6. Applying this method to over 638 398 galaxy samples from the Kilo-Degree Survey Data Release 4 and conducting thorough inspections, we identify 88 Class A, 322 Class B, and 1758 Class C candidates. Conclusions. This hybrid approach significantly enhances the completeness of existing strong gravitational lensing searches and shows great potential for improving future astronomical surveys.

We constrained minimally extended cosmological models with the cosmic shear analysis of the final data release from the Kilo-Degree Survey (KiDS-Legacy) in combination with external probes. Due to the consistency of the KiDS-Legacy analysis with the cosmic microwave background (CMB), we could combine these datasets reliably for the first time. Additionally, we used CMB lensing, galaxy redshift-space distortions, and baryon acoustic oscillations. We assessed, in turn, the effects of spatial curvature, varying neutrino masses, and an evolving dark energy component on cosmological constraints from KiDS-Legacy alone and from KiDS-Legacy combined with external probes. We find KiDS-Legacy to be consistent with the fiducial flat Λ-cold dark matter (ΛCDM) analysis with c ² ∑ m _ν ≤ 1.5 eV, w ₀ = −1.0 ± 0.7, and w _a = −1.3 _−2.0 ^+1.9 while Ω _K = 0.8 _−0.17 ^+0.16 (1 σ bounds) with an almost equal goodness of fit. The w ₀ w _a CDM model is not a significant improvement over ΛCDM when cosmic shear and CMB lensing are combined, yielding a Bayes factor B = 0.07. If all probes are combined, however, B increases to 2.73, corresponding to a 2.6 σ suspiciousness tension. The constraint on S ₈ = σ ₈ √Ω _m /0.3 is robust to opening up the parameter space for cosmic shear. Adding all external datasets to KiDS-Legacy, we find S ₈ = 0.816 ± 0.006 in ΛCDM and S ₈ = 0.837 ± 0.008 in w ₀ w _a CDM for all probes combined.

Ultra-diffuse galaxies (UDGs) are a subset of low-surface-brightness galaxies (LSBGs), showing mean effective surface brightness fainter than 24 mag arcsec ⁻² and a diffuse morphology, with effective radii larger than 1.5 kpc. Due to their elusiveness, using traditional methods over large sky areas is challenging. Here we present a catalog of UDG candidates identified in the full 1350 deg ² area of the Kilo-Degree Survey (KiDS) using deep learning. In particular, we used a previously developed network for the detection of low-surface-brightness systems in the Sloan Digital Sky Survey (SBGnet) and optimized for UDG detection. We trained this new UDG detection network for KiDS (UDGnet-K), with an iterative approach, starting from a small-scale training sample. After training and validation, the UGDnet-K has been able to identify ∼3300 UDG candidates, of which, after visual inspection, we selected 545 high-quality ones. The catalog contains the independent rediscovery of previously confirmed UDGs in local groups and clusters (e.g., NGC 5846 and Fornax), and new discovered candidates in about 15 local systems, for a total of 67 bona fide associations. Besides the value of the catalog per se for future studies of UDG properties, this work shows the effectiveness of an iterative approach to training deep learning tools in presence of poor training samples, due to the paucity of confirmed UDG examples, which we expect to replicate for upcoming all-sky surveys such as those by the Rubin Observatory, Euclid, and the China Space Station Telescope.

Small-scale discrepancies in the standard Lamda cold dark matter paradigm have motivated the exploration of alternative dark matter (DM) models, such as warm dark matter (WDM). In our work, we investigate the constraining power of galaxy scaling relations on cosmological, astrophysical, and WDM parameters using a joint analysis of multiresolution 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 (MW) zoom-in runs exploring a ΛWDM cosmology. To ensure consistency between the different simulation sets, we applied calibrations to account for resolution effects, which allowed us to better exploit the complementary strengths of the two suites. We compared the simulated relations, such as 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 Spitzer Photometry and Accurate Rotation Curves catalog, providing resolved kinematics for nearby spirals, and the Local Volume Database catalog, which includes structural and dynamical measurements for dwarf galaxies in the Local Volume. By applying a bootstrap-based fitting procedure, we show that key cosmological parameters (Ω _m , σ ₈ ) and supernova feedback strength can be recovered with good accuracy, particularly from the uniform-box simulations. Although the WDM particle mass remains unconstrained, the MW zoom-in simulations reveal subtle WDM-induced trends, especially at low stellar masses, in the scaling relations of both the DM mass and the total-to-stellar mass ratio within the stellar half-mass radius. Additionally, we find that the galaxy abundance as a function of total stellar mass shows a measurable dependence on WDM particle mass, with a suppression at log ₁₀ M _* / M _⊙ ≲ 8 that appears separable from the impact of feedback, suggesting this observable is a valuable complementary probe. Our results highlight the importance of combining simulations at multiple resolutions with diverse observational catalogs to jointly constrain baryonic processes and DM properties. In particular, future low-mass galaxy surveys such as Euclid will play a crucial role in tightening the constraints on alternative DM scenarios through joint structural and statistical analyses. At the same time, higher-resolution simulations will be essential to fully capturing the small-scale features and improving the discriminatory power of such analyses, especially in the context of WDM.

Galaxy clusters provide critical constraints on cosmological parameters; however, traditional approaches based on mass–observable scaling relations are subject to systematic uncertainties. Here, we present a machine-learning framework that directly infers cosmological parameters from real eROSITA galaxy cluster observations, bypassing explicit mass calibration. We train a random forest algorithm on Magneticum multi-cosmology hydrodynamical simulations and apply it to observed X-ray properties (gas luminosity, mass, and temperature) at different redshifts from the eROSITA catalogs. This method yields cosmological constraints including the matter density Ωm=0.30^+0.03_-0.02, and the 8 Mpc/h fluctuation amplitude, σ8=0.81^+0.01_-0.01, consistent with current state-of-the-art probes. Unlike other low-redshift cosmological datasets, these parameters show no significant tension with Cosmic Microwave Background inferences from the Planck mission. In contrast, our inference of the Hubble constant, h0=0.710^+0.004_-0.004, shows a significant deviation from the Planck value and lies slightly below most late–Universe determinations, while remaining comfortably consistent with the Tip of the Red Giant Branch (\textit{TRGB}) measurements. This suggests a potential reduction of the early–late Universe Hubble tension. This study represents the first direct cosmological inference from observational cluster data using machine learning trained on multi-cosmology simulations. It establishes an alternative route to precision cosmology using galaxy cluster observations via machine learning, and highlights the potential of large-scale X-ray surveys to deliver independent tests of the standard cosmological model.