arXiv subjects

Crescenzo Tortora

Publications and source records attributed to Crescenzo Tortora.

At least 19 recordsLinked to original sources

arXiv2026

Reducing False Positives in Strong-Lens Searches with Generalized-Mean Consensus of Machine-Learning Ensembles in the Kilo-Degree Survey

Context. In wide-field surveys, the main challenge is not just classifier sensitivity, but the overwhelming number of false positives. Searching for strong lenses among millions to bilions of galaxies produces many contaminants, making the bottleneck for follow-up inspection and building statistically useful lens samples. Aims. We aim to improve the purity of strong-lens candidate selection in KiDS DR4 by combining several classifiers. The objective is to retain high completeness for known candidates while substantially reducing the fraction of non-lenses. Methods. We trained convolutional, Transformer-based, and hybrid classifiers, including Li ResNet+, Swin Transformer variants, Swin-MLP, and DemiLensNet. Their probabilistic outputs were combined at score level using averaging and a generalized mean consensus. The models were tested on simulated KiDS-like lens images and then evaluated on real KiDS DR4 lens candidates embedded in a non-lens sample. Results. On the simulated test set, ensembles show no advantage over the best single models. On the mixed real KiDS test set, the arithmetic mean reduces the false-positive rate at 90% completeness from 0.016-0.020 (the range spanned by the two best individual models) to 0.011 for the seven-model ensemble. The generalized mean reduces it further, to 0.007. Applied to the full LRG and BG samples at the same 90% completeness level, the generalized mean reduces returned candidates by roughly 50% for LRGs and 70% for BGs, relative to the best single model. After visual inspection, we obtain 170 new high-quality candidates (24 Class A and 146 Class B), together with 1706 Class C candidates. Conclusions. Our results demonstrate that the generalized mean consensus of an ML ensemble strategy provides a practical route to reducing the visual inspection workload while preserving a high recovery rate of promising strong-lens candidates.

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arXiv2026

KiDS J1447-0149: The first spatially resolved spectroscopy of a relic galaxy beyond the local universe. An old high-dispersion core embedded in a compact rotating stellar structure

Relic galaxies are the descendants of high-redshift compact quiescent systems. We present the first spatially resolved spectroscopic study of J1447-0149, a massive relic at $z=0.21$, observed with MUSE+AO. We characterize its kinematics and stellar population properties to probe its spatially resolved mass assembly history. We measured stellar kinematics with sub-kpc bin sizes, covering $\sim 1.4$ effective radii. We then used a coarser three-bin configuration informed by the kinematics to recover the higher-order velocity moments, $[α/{\rm Fe}]$, stellar age and metallicity. We reconstructed the star formation history and computed the degree of relicness (DoR). MUSE data reveal a velocity gradient, showing that J1447-0149 is not purely pressure supported. The velocity-dispersion field displays a central peak, reaching $σ_\star=233\pm13\,{\rm km\,s^{-1}}$, substantially larger than previous seeing-limited measurements ($σ_{\star} =187 \pm 9\,{\rm km\,s^{-1}}$). The $h_3$--$V_\star$ anti-correlation and mildly positive $h_4$ values support a composite structure, with a rotating stellar component surrounding a compact dynamically hot core. This central, dispersion-dominated region is also the oldest and most metal-rich component. Its SFH rises rapidly, with the stellar mass assembled within $\sim2\,{\rm Gyr}$ after the Big Bang, and reaches ${\rm DoR}=0.9^{+0.1}_{-0.2}$. The two outer bins have a DoR value of ${\rm DoR}=0.8\pm0.2$, consistent with the central bin, but possibly indicating slightly longer formation times. Spatially resolved spectroscopy has been crucial to confirm the relic nature of J1447-0149, linking resolved morphology, kinematics, and stellar populations to constrain the early assembly of its central spheroid and surrounding disk.

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arXiv2026

Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets

*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 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 1,758 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.

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arXiv2026

LenNet: Direct Detection and Localization of Strong Gravitational Lenses in Wide-Field Sky Survey Images

Strong gravitational lenses are invaluable tools for addressing fundamental questions in astrophysics, from the nature of dark matter to the expansion of the universe. While current sky surveys have successfully identified thousands of lens candidates, the search methods employed face a critical challenge. The conventional approach relies on a "crop-and-classify" strategy, where small images are first cut out around billions of potential host galaxies before being individually classified. This process creates a significant computational and storage bottleneck that is unsustainable for future large-scale surveys. To overcome this limitation, we propose LenNet, an object detection model that identifies lenses directly within large, original survey images. Our method completely bypasses the inefficient cropping step by framing the problem as a direct detection and localization task. We initially train LenNet on simulated data to learn the complex features of gravitational lenses and then use transfer learning to fine-tune the model on a limited set of real, labeled examples from the Kilo-Degree Survey (KiDS). Our experiments show that LenNet performs remarkably well on real survey data, validating its potential as a highly efficient and scalable solution for lens discovery in massive astronomical surveys.

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arXiv2026

Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands

We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy-galaxy strong lensing images. By integrating synthetic Spectral Energy Distribution (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 (PSF), magnitude limits, and zero points, to replicate specific survey conditions, thereby enabling robust cross-survey joint analyses. As an application, we simulate multi-band images for KiDS, 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 $\mathcal{O}(10^3)$ over traditional CPU-based pipelines, demonstrating the potential feasibility of joint gradient-based lens modeling across next-generation surveys.

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arXiv2026

E-INSPIRE - II. Finding relics from wide-sky multi-band surveys: A proof-of-concept machine learning regression algorithm

In this second paper of the E-INSPIRE series, we train a machine-learning-based regression on $\sim430$ nearby ($z<0.5$) ultra-compact massive galaxies (UCMGs) with spectroscopically inferred kinematics, stellar population parameters and a measured ``degree of relicness'' (DoR). Our goal is to investigate how robustly the spectroscopically inferred DoR can be statistically reconstructed from observable galaxy properties, and to explore the potential applicability of this framework to future wide-area surveys. We test several regression algorithms finding that Support Vector Regression (SVR) provides the best performance. We explore multiple input feature configurations, from a minimal set including only age and metallicity to more comprehensive ones incorporating stellar population parameters, kinematics, structural properties, and the associated uncertainties. All tested models achieve similarly high performance on the training set ($R^2\ge0.81$), except for the minimal configuration ($R^2\sim0.78$). When evaluated on an independent INSPIRE sample of 52 UCMGs, the predictive power remains robust, although with increased model-to-model variation. The DoR distribution shows three regimes, with low (DoR$<0.3$) and high (DoR$>0.6$) values sparsely populated, leading to mild regression shrinkage toward intermediate values. However, this behaviour enables a conservative selection strategy: galaxies with predicted DoR$\ge0.6$ are strongly biased toward genuine extreme relics, making them prime targets for follow-up observations. This proof-of-concept confirms that the spectroscopically inferred DoR is robustly connected to observable stellar population and kinematical properties, and provides a first step toward future relic-candidate selection strategies in large photometric and spectroscopic surveys.

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arXiv2026

Strong Lensing Tomography: Double and pseudo multi-source plane strong gravitational lensing to constrain dark energy

Tomographic measurements of gravitational lensing with different lens and source redshift distributions contain crucial information about the universe's relative expansion rate, and hence dark energy. While this technique is well-established in weak lensing, its application to strong lensing has traditionally focused on Double Source Plane Lenses (DSPLs). However, DSPLs are exceedingly rare and fundamentally limited by the Mass-Sheet Degeneracy (MSD), a systematic uncertainty underexplored in previous literature. To overcome these challenges, we introduce Pseudo Double-Source Plane Lenses (PDSPLs): pairs of independent single-source plane lenses with self-similar deflectors. This generalizes the DSPL formalism to the $\sim 10^5$ galaxy-galaxy lenses expected from upcoming surveys like LSST, Euclid, and Roman. Unlike true DSPLs, PDSPLs are free from the intermediate source mass problem by construction, eliminating the associated secondary MSD and the need for multi-plane ray tracing. We incorporate the deflector galaxy's MSD into a hierarchical forecasting framework, demonstrating that this degeneracy severely degrades constraints from small DSPL samples, thus motivating our PDSPL statistical approach. We forecast constraints on the dark energy equation of state under a Flat $w_0w_a$CDM cosmology. The LSST 10-year photometric sample alone achieves $σ(w_0) \sim 0.45$, while simultaneously constraining the MSD parameter and deflector power-law slope to $\sim 2\%$. Adding a prior $\mathcal{N}(0.3, 0.05)$ on $Ω_{\rm m}$ -- simulating combination with external probes like CMB, BAO, or SNe Ia -- tightens this to $σ(w_0) \sim 0.29$, competitive with current Stage III weak lensing analyses. Notably, this massive photometric sample outperforms smaller subsets with precise spectroscopic follow-up (e.g., from 4MOST), confirming statistical volume dominates over per-pair precision.

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arXiv2026

Reconciling the Fundamental Plane of Early-Type Galaxies with hydrodynamical simulations: The case of IllustrisTNG100-1

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 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, bottom-heavy IMF variations produce FP coefficients fully consistent with observational constraints for both direct and orthogonal fits. 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.

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arXiv2026

Kinematically cold and warm planetary nebulae samples, HII regions and supernovae remnants in the disc of the face-on spiral galaxy NGC 628 (M74) -- The Planetary Nebulae Spectrograph with the H$α$ arm

We present the results for the galaxy NGC 628 observed with the Planetary Nebulae Spectrograph (PN.S) equipped with the H$α$ arm. With the third PN.S arm, the H$α$ arm, we measure the H$α$ fluxes, in addition to fluxes and line-of-sight velocities (LOSV) of monochromatic spatially unresolved [OIII] 5007Å sources. The narrow band color ([OIII] 5007Å-H$α$) vs m5007 magnitude diagram separates planetary nebulae (PNe) from single compact ionized HII regions and supernovae remnants (SNRs), which also emit in [OIII]5007 Å. The goals are to detect bona-fide PNe in the face-on spiral galaxy NGC 628 (M74) so that we can measure the velocity dispersion of the stars perpendicular to the main plane of the disc. This study validates the empirical selection criteria for PNe with the PN.S in star forming discs. We classified 442 PNe and 251 spatially isolated, unresolved HII regions: the PN.S with the H$α$ arm increased the number of known PNe by a factor 4. We find evidence for two kinematically distinct PN populations in the NGC 628 disc. The kinematically cold PN population dominates the PN luminosity function close to the bright cut-off magnitude, indicating that the PN massive, short-lived progenitors dominate the PNLF bright cut-off in NGC 628. The warmer PN component increasingly dominates at fainter magnitudes. The velocity dispersion orthogonal to the disc plane are σz,cold = 8.8 kms-1 and σz,warm =26.1 kms-1 respectively, over a range of radii 80 to 425 arcsec. These components contribute with the ratio 46% (cold) and 54% (warm). Once the velocity dispersion of the old component is matched with the population's scale height, the decomposition of the rotation curve for NGC 628 leads to a maximal disc, with the rotation of the baryonic component accounting for 78% of the total rotational velocity in NGC 628.

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arXiv2026

Cosmology with galaxy clusters using machine learning. Application to eROSITA Data

Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum multi-cosmology hydrodynamical simulations. We apply the trained ML algorithm to observed X-ray features (gas luminosity, mass, and temperature) at different redshifts from the eROSITA eFEDS and eRASS1 catalogs. Results: We obtain cosmological constraints with precision comparable to those from standard analyses, such as weak lensing and cluster abundances. We infer $Ω_{\rm m}=0.30^{+0.03}_{-0.02}$, $σ_8=0.81\pm0.01$, and $h_0=0.710\pm0.004$. The recovered parameters show no tension in the $Ω_{\rm m}-σ_8$ space, but a significant deviation of $h_0$ from the Planck estimates. These inferences remain rather stable against variations of the input observable set and parameter space coverage. These results indicate that correlations among intracluster properties contain cosmological information beyond that encoded in the cluster abundance alone, which can be captured by machine learning trained on multi-cosmology simulations. Conclusions: ML algorithms trained on multi-cosmology hydrodynamical simulations can effectively infer cosmological parameters directly from galaxy cluster data. This is a change of paradigm in the context of cosmological parameter inferences. This approach complements traditional cluster-count analyses and is particularly suited to large upcoming surveys, where systematic uncertainties in mass calibration may otherwise dominate the error budget. It also highlights the potential of large-scale X-ray surveys to deliver independent tests of the standard cosmological model.

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arXiv2026

Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations

Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited excursions over small volumes at higher redshifts. Next-generation large sky space surveys in optical, e.g. from the China Space Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID mission, will produce a gigantic leap in these studies as they will provide deep, high-quality photometric images over more than 15000 deg2 of the sky, including billions of galaxies. Here, we extend the use of the Galaxy Light profile neural Network (GaLNet) to predict 2-Sérsic model parameters, specifically from CSST data. We simulate point-spread function (PSF) convolved galaxies, with realistic B-D parameter distributions, on CSST mock observations to train the new GaLNet and predict the structural parameters (e.g. magnitude, effective radius, Sersic index, axis ratio, etc.) of both bulge and disk components. We find that the GaLNet can achieve very good accuracy for most of the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1. The best accuracy is obtained for magnitudes, implying accurate bulge-to-total (B/T) estimates. To further forecast the CSST performances, we also discuss the results of the 1-Sérsic GaLNet and show that CSST half-depth data will allow us to derive accurate 1-component models up to $r\sim$24 and redshift z$\sim$1.7.

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arXiv2025

Unlocking the physics of dwarf galaxies in the 2040s: The case for a next-generation wide-field spectroscopic facility with fibres and IFUs

Dwarf galaxies ($M_{\star} \lesssim 10^{9} M_{\odot}$) are the most numerous galaxies in the Universe and critical probes of dark matter, baryonic feedback, and galaxy formation. Despite significant progress from wide-field imaging surveys, the majority of dwarf candidates beyond the Local Group will lack spectroscopic follow-up, leaving fundamental questions about their internal kinematics, stellar populations, chemical enrichment, and dark matter content unresolved. Existing and planned facilities cannot efficiently provide the necessary spectroscopy for low-surface-brightness dwarfs over wide areas. We advocate for a dedicated large-aperture ($\geq 20$ m), wide-field, highly multiplexed spectroscopic facility with deployable or monolithic IFUs, capable of high signal-to-noise observations down to $I_{\rm E} \gtrsim 22-23$ mag. Such a facility would enable transformative studies of dark matter cores, baryonic feedback, tidal interactions, environmental effects, and stellar populations, extending the spectroscopic exploration of low-mass galaxies to $z \sim 1.5$, and providing decisive tests of $Λ$CDM and alternative dark matter models. Beyond dwarfs, this capability would impact galaxy evolution, strong and weak lensing studies, and cosmology, ensuring that imaging data from the 2030s and 2040s can be fully exploited.

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arXiv2025

Archaeological investigation of galaxies' evolutionary history in the cosmic middle ages

The cosmic Middle Ages, spanning the last 8-10 Gyr of the Universe, is a critical period in which massive early-formed systems coexist with global star formation quenching in less massive galaxies, yet galaxies experience further dynamical, morphological and chemical evolution. Understanding the relative role of internal drivers and of interaction with the evolving large-scale structures remains a highly complex and unsettled issue. To make transformative progress on these questions we must characterize the physical and kinematic properties (integrated and spatially resolved) of stellar populations in galaxies, fossil record of their past star formation and assembly histories, together with gas properties, across a wide range of masses and environmental scales, over this critical cosmic epoch. Volume-representative samples of 10^6 galaxies down to 10^9 solar masses are essential to fully trace the complex interplay between physical processes and to physically connect progenitor and descendant galaxy populations. This demands a deep and extensive survey with high signal-to-noise, medium-resolution, rest-frame optical spectroscopy. Current and planned facilities in the 2020-2030s cannot simultaneously achieve the required sample size, spectral quality, mass limit, and spatial coverage. A dedicated large-aperture spectroscopic facility with wide-area high-multiplex MOS and large field-of-view IFU is needed to provide transformative insights into the physical mechanisms regulating star formation and galaxy evolution.

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arXiv2025

Total and dark mass from observations of galaxy centers with Machine Learning

The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have limitations. We propose a novel approach, based on Random Forest, to make predictions on the total and dark matter content of galaxies using simple observables from imaging and spectroscopic surveys. We use catalogs of multi-band photometry, sizes, stellar mass, kinematic "measurements" (features) and dark matter (targets) of simulated galaxies, from Illustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machine Learning Algorithm (Mela). We separate the simulated sample in passive early-type galaxies (ETGs), both "normal" and "dwarf", and active late-type galaxies (LTGs) and show that the mass estimator can accurately predict the galaxy dark masses inside the effective radius in all samples. We finally test the mass estimator against the central mass estimates of a series of low redshift (z$\leq$0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf galaxies, derived with standard dynamical methods based on Jeans equations. Dynamical masses are reproduced within 0.30 dex ($\sim2σ$), with a limited fraction of outliers and almost no bias. This is independent of the sophistication of the kinematical data collected (fiber vs. 3D spectroscopy) and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations, virial theorem). This makes Mela a powerful alternative to predict the mass of galaxies of massive stage-IV surveys' datasets.

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arXiv2025

Using Deep Learning Methods to Detect for Ultra-diffuse Galaxies in KiDS

Ultra-diffuse Galaxies (UDGs) are a subset of Low Surface Brightness Galaxies (LSBGs), showing mean effective surface brightness fainter than $24\ \rm mag\ \rm arcsec^{-2}$ and a diffuse morphology, with effective radii larger than 1.5 kpc. Due to their elusiveness, traditional methods are challenging to be used over large sky areas. Here we present a catalog of ultra-diffuse galaxy (UDG) candidates identified in the full 1350 deg$^2$ area of the Kilo-Degree Survey (KiDS) using deep learning. In particular, we use a previously developed network for the detection of low surface brightness systems in the Sloan Digital Sky Survey \citep[LSBGnet,][]{su2024lsbgnet} and optimised for UDG detection. We train 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 $\sim3300$ UDG candidates, among which, after visual inspection, we have selected 545 high-quality ones. The catalog contains independent re-discovery 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 {\it bona fide} associations. Besides the value of the catalog {\it 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 like Rubin Observatory, Euclid and the China Space Station Telescope.

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arXiv2025

Optical+NIR analysis of a Newly Confirmed Einstein ring at z$\sim$1 from the Kilo-Degree Survey: Dark matter fraction, total and dark matter density slope and IMF

We report the spectroscopic confirmation of a bright blue Einstein ring in the Kilo Degree Survey (KiDS) footprint: the Einstein ``blue eye''. Spectroscopic data from X-Shooter at the Very Large Telescope (VLT) show that the lens is a typical early-type galaxy (ETG) at $z_l=0.9906$, while the background source is a Ly$α$ emitter at $z_s=2.823$. The reference lens modeling was performed on a high-resolution $Y-$band adaptive-optics image from HAWK-I at VLT. Assuming a singular isothermal ellipsoid (SIE) total mass density profile, we inferred an Einstein radius $R_{Ein}=10.47 \pm 0.06$ kpc. The average slope of the total mass density inside the Einstein radius, as determined by a joint analysis of lensing and isotropic Jeans equations is $γ_{tot}=2.14^{+0.06}_{-0.07}$, showing no systematic deviation from the slopes of lower redshift galaxies, This can be the evidence of ETGs developing through dry mergers plus moderate dissipationless accretion. Stellar population analysis with 8-band ($gri$ZYJHK$s$) photometries from KiDS and VIKING shows that the total stellar mass of the lens is $M*=(3.95\pm 0.35)\times 10^{11} M_\odot$ (Salpeter Initial Mass Function, IMF), implying a dark matter fraction inside the effective radius to be $f_{\rm DM}=0.307\pm 0.151$. We finally explored the dark matter halo slope and found a strong degeneracy with the dynamic stellar mass. Dark matter adiabatic contraction is needed to explain the posterior distribution of the slope unless IMF heavier than Salpeter is assumed.

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arXiv2025

The catalogue of virtual early-type galaxies from IllustrisTNG: validation and real observation consistency

Early-type galaxies (ETGs) are reference systems to understand galaxy formation and evolution processes. The physics of their formation and internal dynamics are codified in well-known scaling relations. In this context, cosmological hydrodynamical simulations play an important role in probing the physical origins of scaling relations by providing a controlled environment to study the formation and evolution of galaxies, linking their internal dynamics to underlying physical processes, and testing the robustness of observational inference methods. In this work, we present the closest-to-reality sample of ETGs from the IllustrisTNG100-1 simulation, dubbed "virtual-ETGs", based on an observational-like algorithm that combines standard projected and three-dimensional galaxy structural parameters. We extract 2D photometric information by projecting the galaxies' light into three planes and modelling them via Sérsic profiles. Aperture velocity dispersions, corrected for softened central dynamics, are calculated along the line-of-sight orthogonal to the photometric projection plane. Central mass density profiles assume a power-law model, while 3D masses remain unmodified from the IllustrisTNG catalogue. The final catalogue includes $10121$ galaxies at redshifts $z \leq 0.1$. By comparing the virtual properties with observations, we find that the virtual-ETG scaling relations (e.g., size-mass, size-central surface brightness, and Faber-Jackson), central density slopes, and scaling relations among total density slopes and galaxy structural parameters are generally consistent with observations. We make the virtual-ETG publicly available for galaxy formation studies and plan to use this sample as a training set for machine learning tools to infer galaxy properties in future imaging and spectroscopic surveys.

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arXiv2025

Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale

The intrinsic alignment (IA) of galaxies is a major astrophysical contaminant to weak gravitational lensing measurements, and the study of its dependence on galaxy properties helps provide meaningful physical priors that aid cosmological analyses. This work studied for the first time the dependence of IA on galaxy structural parameters. We measured the IA of bright galaxies, selected on apparent r-band magnitude r<20, in the Kilo-Degree Survey (KiDS). Machine-learning-based photometric redshift estimates are available for this galaxy sample that helped us obtain a clean measurement of its IA signal. We supplemented this sample with a catalogue of structural parameters from Sersic profile fits to the surface-brightness profiles of the galaxies. We split the sample on galaxy intrinsic colour, luminosity, and Sersic index, and we fitted the non-linear linear alignment model to galaxy position-shape projected correlation function measurements on large scales. We observe a power-law luminosity dependence of the large-scale IA amplitude, $A_{IA}$, for both the red and high-Sersic-index ($n_s>2.5$) samples, and find no significant difference between the two. We measure an $\sim1.5σ$ lower $A_{IA}$ for red galaxies that also have a Sersic index of $n_s<4$ compared to the expected amplitude predicted using the sample's luminosity. We also probe the IA of red galaxies as a function of galaxy scale by varying the radial weight employed in the shape measurement. On large scales (above 6 Mpc/$h$), we do not detect a significant difference in the alignment. On smaller scales, we observe that IA increase with galaxy scale, with outer galaxy regions showing stronger alignments than inner regions. Finally, for intrinsically blue galaxies, we find $A_{IA}=-0.67\pm1.00$, which is consistent with previous works, and we find IA to be consistent with zero for the low-Sersic-index ($n_s<2.5$) sample.

astro-ph.CO↗