arXiv subjects

Crescenzo Tortora

Publications and source records attributed to Crescenzo Tortora.

At least 37 records · Page 2Linked to original sources

arXiv2025

Quantifying the Detectability of Milky Way Satellites with Image Simulations: a Case Study with KiDS

Ultra-faint dwarf galaxies, which can be detected as resolved satellite systems of the Milky Way, are critical to understanding galaxy formation, evolution, and the nature of dark matter, as they are the oldest, smallest, most metal-poor, and most dark matter-dominated stellar systems known. Quantifying the sensitivity of surveys is essential for understanding their capability and limitations in searching for ultra-faint satellites. In this paper, we present the first study of the image-level observational selection function for Kilo-Degree Survey (KiDS) based on the Synthetic UniveRses For Surveys (surfs)-based KiDS-Legacy-Like Simulations. We generate mock satellites and simulate images that include resolved stellar populations of the mock satellites and the background galaxies, capturing realistic observational effects such as source blending, photometric uncertainties, and star-galaxy separation. The matched-filter method is applied to recover the injected satellites. We derive the observational selection function of the survey in terms of the luminosity, half-light radius, and heliocentric distance of the satellites. Compared to a catalogue-level simulation as used in previous studies, the image-level simulation provides a more realistic assessment of survey sensitivity, accounting for observational limitations that are neglected in catalogue-level simulations. The image-level simulation shows a detection loss for compact sources with a distance $d \gtrsim 100~\rm kpc$. We argue that this is because compact sources are more likely to be identified as single sources rather than being resolved during the source extraction process.

astro-ph.CO↗

arXiv2025

INSPIRE: INvestigating Stellar Population In RElics VIII. Emission lines and UV colours in ultra-compact massive galaxies

We report the discovery of emission lines in the optical spectra of ultra-compact massive galaxies (UCMGs) from INSPIRE, including relics, which are the oldest galaxies in the Universe. Emission-lines diagnostic diagrams suggest that all these UCMGs, independently of their star formation histories, are `retired galaxies'. They are inconsistent with being star-forming but lie in the same region of shock-driven emissions or photoionisation models, incorporating the contribution from post-asymptotic giant branch (pAGB) stars. Furthermore, all but one INSPIRE objects have a high [OII]/Hα ratio, resembling what has been reported for normal-size red and dead galaxies. The remaining object (J1142+0012) is the only one to show clear evidence for strong active galactic nucleus activity from its spectrum. We also provide near-UV (far-UV) fluxes for 20 (5) INSPIRE objects that match in GALEX. Their NUV-r colours are consistent with those of galaxies lying in the UV green valley, but also with the presence of recently (<0.5 Gyr) formed stars at the sub-percent fraction level. This central recent star formation could have been ignited by gas that was originally ejected during the pAGB phases and then re-compressed and brought to the core by the ram-pressure stripping of Planetary Nebula envelopes. Once in the centre, it can be shocked and re-emit spectral lines.

astro-ph.GA↗

arXiv2025

E-INSPIRE -- I. Bridging the gap with the local Universe: Stellar population of a statistical sample of ultra-compact massive galaxies at $z<0.3$

This paper presents the first effort to Extend the Investigation of Stellar Populations In RElics (E-INSPIRE). We present a catalogue of 430 spectroscopically-confirmed ultra-compact massive galaxies (UCMGs) from the Sloan Digital Sky Survey at redshifts $0.01 25\%$) of their stellar mass at $z<2$. We confirm that an efficient method of finding relics is to select UCMGs with large velocity dispersion values but we believe that the most efficient way is to select high velocity dispersion objects that also have super-solar metallicities and high [Mg/Fe].

astro-ph.GA↗

arXiv2025

Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation)

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes P$_{\rm mean}$ and IC$_{\rm mean}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores, filtering based on Information Content, and segmentation score. Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data.

astro-ph.GA↗

arXiv2025

Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online. We have also included an excellent quad candidate in the appendix, discovered serendipitously during the fine-tuning process of the CNN.

astro-ph.GA↗

arXiv2024

CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- II. Constraining cosmology and astrophysical processes with early- and late-type galaxies

Physical processes impact galaxy formation and evolution in diverse ways, requiring validation of their implementation in cosmological simulations through comparisons with real data across various galaxy types and properties. In this second paper of the CASCO series, we compare the structural properties and dark matter (DM) content of early-type galaxies from the CAMELS IllustrisTNG simulations to three observational datasets (SPIDER, $\textrm{ATLAS}^{\textrm{3D}}$, and MaNGA DynPop), to constrain cosmological and astrophysical feedback parameters, contrasting these results with those obtained for late-type galaxies. We analyze the size-, internal DM fraction-, and DM mass-stellar mass relations, identifying the best-fit simulation for each dataset. For SPIDER, we find cosmological parameter values consistent with literature and results obtained from the comparison between simulations and late-type galaxies, with supernova feedback parameters differing from results derived for late-type galaxies. For $\textrm{ATLAS}^{\textrm{3D}}$, cosmological parameter results align with SPIDER, while supernova feedback parameters are more consistent with late-type galaxies results. MaNGA DynPop yields extreme cosmological parameter values but similar supernova feedback results to $\textrm{ATLAS}^{\textrm{3D}}$. However, no single simulation matches the full range of observational trends, especially when combining early- and late-type galaxies from MaNGA DynPop. These findings highlight the limitations of simulations in reproducing diverse galaxy properties, underscoring the challenge of capturing the complexity of galaxy formation across all types.

astro-ph.GA↗

arXiv2024

Leveraging Transfer Learning for Astronomical Image Analysis

The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibilities offered by transfer learning techniques in addressing these challenges across various domains of astronomical research. We present a set of recent applications of transfer learning methods for astronomical tasks based on the usage of a pre-trained convolutional neural networks. The examples shortly discussed include the detection of candidate active galactic nuclei (AGN), the possibility of deriving physical parameters for galaxies directly from images, the identification of artifacts in time series images, and the detection of strong lensing candidates and outliers. We demonstrate how transfer learning enables efficient analysis of complex astronomical phenomena, particularly in scenarios where labeled data is scarce. This kind of method will be very helpful for upcoming large-scale surveys like the Rubin Legacy Survey of Space and Time (LSST). By showcasing successful implementations and discussing methodological approaches, we highlight the versatility and effectiveness of such techniques.

astro-ph.IM↗

arXiv2024

Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions

Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multi-network deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions for classified objects in each of them. It also provides redshift errors, using a network-of-networks that reproduces a Monte Carlo test on each spectrum, by randomizing their weight initialization. As a demonstration of the capability of the deep learning pipeline, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4% average classification accuracy over the 13 classes (larger than 90% for the majority of them), and an average redshift error of approximately 0.23% for galaxies and 2.1% for quasars. We further train/test the same pipeline to classify spectra and predict redshifts for a sample of 200k 4MOST mock spectra and 21k publicly released DESI spectra. On 4MOST mock data, we reach 93.4% accuracy in 10-class classification and an average redshift error of 0.55% for galaxies and 0.3% for active galactic nuclei. On DESI data, we reach 96% accuracy in (star/galaxy/quasar only) classification and an average redshift error of 2.8% for galaxies and 4.8% for quasars, despite the small sample size available. GaSNet-II can process ~40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses of Stage-IV survey observations and an ideal tool for feedback loops aimed at night-by-night survey strategy optimization.

astro-ph.IM↗

arXiv2024

VST-SMASH: the VST Survey of Mass Assembly and Structural Hierarchy

The VLT Survey Telescope Survey of Mass Assembly and Structural Hierarchy (VST-SMASH) aims to detect tidal features and remnants around very nearby galaxies, a unique and essential diagnostic of the hierarchical nature of galaxy formation. Leveraging optimal sky conditions at ESO's Paranal Observatory, combined with the VST's multi-band optical filters, VST-SMASH aims to be the definitive survey of stellar streams and tidal remnants in the Local Volume, targeting a low surface-brightness limit of $μ\sim$ 30 mag arcsec$^{-2}$ in the g and r bands, and $μ\sim$ 28 mag arcsec$^{-2}$ in the i band, in a volume-limited sample of local galaxies within 11 Mpc and the Euclid footprint.

astro-ph.GA↗

arXiv2024

Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations

We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most important result of this work is that the GGSL-UNet can accurately reconstruct real ground-based lensing systems from the Kilo Degree Survey (KiDS) in one second. We also test the GGSL-UNet on space-based (HST) lenses from BELLS GALLERY, and obtain comparable accuracy of standard lens modeling tools. Finally, we calculate the magnitudes from the reconstructed deflector and source images and use this to derive photometric redshifts (photo-z), with the photo-z of the deflector well consistent with spectroscopic ones. This first work, demonstrates the great potential of the generative network for lens finding, image denoising, source segmentation, and decomposing and modeling of strong lensing systems. For the upcoming ground- and space-based surveys, the GGSL-UNet can provide high-quality images as well as geometry and redshift information for precise lens modeling, in combination with classical MCMC modeling for best accuracy in the galaxy-galaxy strong lensing analysis.

astro-ph.GA↗

arXiv2024

INSPIRE: INvestigating Stellar Population In RElics -- VII. The local environment of ultra-compact massive galaxies

Relic galaxies, the oldest ultra-compact massive galaxies (UCMGs), contain almost exclusively "pristine" stars formed during an intense star formation (SF) burst at high redshift. As such, they allow us to study in detail the early mechanism of galaxy assembly in the Universe. Using the largest catalogue of spectroscopically confirmed UCMGs for which a degree of relicness (DoR) had been estimated, the INSPIRE catalogue, we investigate whether or not relics prefer dense environments. The objective of this study is to determine if the DoR, which measures how extreme the SF history was, and the surrounding environment are correlated. In order to achieve this goal, we employ the AMICO galaxy cluster catalogue to compute the probability for a galaxy to be a member of a cluster, and measure the local density around each UCMG using machine learning-based photometric redshifts. We find that UCMGs can reside both in clusters and in the field, but objects with very low DoR (< 0.3, i.e., a relatively extended SF history) prefer under-dense environments. We additionally report a correlation between the DoR and the distance from the cluster centre: more extreme relics, when located in clusters, tend to occupy the more central regions of them. We finally outline potential evolution scenarios for UCMGs at different DoR to reconcile their presence in both clusters and field environments

astro-ph.GA↗

arXiv2024

Multi-band analysis of strong gravitationally lensed post-blue nugget candidates from the Kilo-Degree Survey

During the early stages of galaxy evolution, a significant fraction of galaxies undergo a transitional phase between the ``blue nugget" systems, which arise from the compaction of large, active star-forming disks, and the ``red nuggets", which are red and passive compact galaxies. These objects are typically only observable with space telescopes, and detailed studies of their size, mass, and stellar population parameters have been conducted on relatively small samples. Strong gravitational lensing can offer a new opportunity to study them in detail, even with ground-based observations. In this study, we present the first 6 \textit{bona fide} sample of strongly lensed post-blue nugget (pBN) galaxies, which were discovered in the Kilo Degree Survey. By using the lensing-magnified luminosity from optical and near-infrared bands, we have derived robust structural and stellar population properties of the multiple images of the background sources. \rui{The pBN galaxies have very small sizes of $\rm R_{eff}<1.3$ kpc, high mass density inside 1 kpc of $ \rm \log (Σ_1/M_{\odot} \mathrm{kpc}^{-2})>9.3$, and low specific star formation rates of $\log (\mathrm{sSFR/Gyr})\lesssim0$, The size-mass and $Σ_1$-mass relations of this sample are consistent with those of the red nuggets, while their sSFR is close to the lower end of compact star-forming blue nugget systems at the same redshift, suggesting a clear evolutionary link between them.

astro-ph.GA↗

arXiv2024

The YMCA (Yes, Magellanic Clouds Again) survey: probing the outer regions of the Magellanic system with VST

The Magellanic Clouds (MCs) are the Milky Way's most massive dwarf satellites. As they also represent the closest pair of galaxies in an ongoing tidal interaction, while simultaneously infalling into the Milky Way halo, they provide a unique opportunity to study in detail an ongoing three-body encounter. We present the ``YMCA (Yes, Magellanic Clouds Again) survey: probing the outer regions of the Magellanic system with VST'' based on deep optical photometry carried out with the VLT Survey Telescope (VST). YMCA targeted 110 square degrees, in the g and i filters, in the periphery of both the MCs, including a long strip in between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC). The photometry of YMCA is sufficiently deep (50\% complete down to $g \simeq 23.5-24.0$~mag) to allow for a detailed analysis of main-sequence stars in regions of the MCs remained relatively unexplored at these faint magnitudes. The resulting colour-magnitude diagrams reveal that the outskirts of the MCs are predominantly characterized by intermediate-age and old stellar populations, with limited or negligible evidence of recent star formation. The analysis of the age distribution of star clusters (SCs) within the surveyed area, both already known and newly discovered candidates, hints at a close fly-by between the LMC and SMC that occurred $\simeq 2.5-3.0$~Gyr ago, in agreement with previous results. We also report the discovery of candidate SCs with ages within the so-called ``age-gap'', questioning its real existence.

astro-ph.GA↗

arXiv2024

INSPIRE: INvestigating Stellar Population In RElics VI -- The low-mass end slope of the stellar Initial Mass Function and chemical composition

The INSPIRE project has built the largest sample of ultra-compact massive galaxies (UCMGs) at 0.1 2, and the other stellar population parameters.We use the Full-Index-Fitting method to fit the INSPIRE spectra to single stellar population (SSP) models. This allows us to measure, for the first time, the low-mass end slope of the IMF, as well as stellar metallicity [M/H], [Mg/Fe], [Ti/Fe] and [Na/Fe] ratios, and study correlations between them and the DoR. Similarly to normal-sized galaxies, UCMGs with larger stellar masses have overall higher metallicities. We found a correlation between the low-mass end of the IMF slope and the DoR, that, however, breaks down for systems with a more extended SFH. An even stronger dependency is found between the IMF and the fraction of mass formed at high-z. At equal velocity dispersion and metallicity, galaxies with a higher DoR have a dwarf-richer IMF than that of low-DoR counterparts. This might indicate that the cosmic epoch and formation mechanisms influence the fragmentation of the star formation cloud and hence might be the explanation for IMF variations detected in massive ETGs.

astro-ph.GA↗

arXiv2024

The TNG50-SKIRT Atlas: post-processing methodology and first data release

Galaxy morphology is a powerful diagnostic to assess the realism of cosmological hydrodynamical simulations. Determining the morphology of simulated galaxies requires the generation of synthetic images through 3D radiative transfer post-processing that properly accounts for different stellar populations and interstellar dust attenuation. We use the SKIRT code to generate the TNG50-SKIRT Atlas, a synthetic UV to near-infrared broadband image atlas for a complete stellar-mass selected sample of 1154 galaxies extracted from the TNG50 cosmological simulation at $z=0$. The images have a high spatial resolution (100 pc) and a wide field of view (160 kpc). In addition to the dust-obscured images, we also release dust-free images and physical parameter property maps with matching characteristics. As a sanity check and preview application we discuss the UVJ diagram of the galaxy sample. We investigate the effect of dust attenuation on the UVJ diagram and find that it affects both the star-forming and the quiescent galaxy populations. The quiescent galaxy region is polluted by younger and star-forming highly inclined galaxies, while dust attenuation induces a separation in inclination of the star-forming galaxy population, with low-inclination galaxies remaining at the blue side of the diagram and high-inclination galaxies systematically moving towards the red side. This image atlas can be used for a variety of other applications, including galaxy morphology studies and the investigation of local scaling relations. We publicly release the images and parameter maps, and we invite the community to use them.

astro-ph.GA↗

arXiv2024

The TNG50-SKIRT Atlas: wavelength dependence of the effective radius

Galaxy sizes correlate with many other important properties of galaxies, and the cosmic evolution of galaxy sizes is an important observational diagnostic for constraining galaxy evolution models. The effective radius is probably the most widely used indicator of galaxy size. We used the TNG50-SKIRT Atlas to investigate the wavelength dependence of the effective radius of galaxies at optical and near-infrared (NIR) wavelengths. We find that, on average, the effective radius in every band exceeds the stellar mass effective radius, and that this excess systematically decreases with increasing wavelength. The optical g-band (NIR Ks-band) effective radius is on average 58% (13%) larger than the stellar mass effective radius. Effective radii measured from dust-obscured images are systematically larger than those measured from dust-free images, although the effect is limited (8.7% in the g-band, 2.1% in the Ks-band). We find that stellar population gradients are the dominant factor (about 80%) in driving the wavelength dependence of the effective radius, and that differential dust attenuation is a secondary factor (20%). Comparing our results to recent observational data, we find offsets in the absolute values of the median effective radii, up to 50% for the population of blue galaxies. We find better agreement in the slope of the wavelength dependence of the effective radius, with red galaxies having a slightly steeper slope than green-blue galaxies. Comparing our effective radii with those of galaxies from the Siena Galaxy Atlas in separate bins in z-band absolute magnitude and g-z colour, we find excellent agreement for the reddest galaxies, but again significant offsets for the blue populations: up to 70% for galaxies around Mz=-21.5. This difference in median effective radius for the bluer galaxies is most probably due to (abridged...).

astro-ph.GA↗

arXiv2023

New Kids in Town. Sextans~II: a new stellar system in the outskirts of the Milky Way

We report on the discovery of a significant and compact over-density of old and metal-poor stars in the KiDS survey (data release 4). The discovery is confirmed by deeper HSC-SSC data revealing the old Main Sequence Turn-Off of a stellar system located at a distance from the sun of $D_{\sun}=145^{+14}_{-13}$~kpc in the direction of the Sextans constellation. The system has absolute integrated magnitude ($M_V=-3.9^{+0.4}_{-0.3}$), half-light radius ($r_h=193^{+61}_{-46}$~pc), and ellipticity ($e=0.46^{+0.11}_{-0.15}$) typical of Ultra Faint Dwarf galaxies (UFDs). The central surface brightness is near the lower limits of known local dwarf galaxies of similar integrated luminosity, as expected for stellar systems that escaped detection until now. The distance of the newly found system suggests that it is likely a satellite of our own Milky Way, consequently, we tentatively baptise it Sextans~II (KiDS-UFD-1).

astro-ph.GA↗

arXiv2023

Toward a stellar population catalog in the Kilo Degree Survey: the impact of stellar recipes on stellar masses and star formation rates

The Kilo Degree Survey (KiDS) is currently the only sky survey providing optical ($ugri$) plus near-infrared (NIR, $ZYHJK_S$) seeing matched photometry over an area larger than 1000 $\rm deg^2$. This is obtained by incorporating the NIR data from the VISTA Kilo Degree Infrared Galaxy (VIKING) survey, covering the same KiDS footprint. As such, the KiDS multi-wavelength photometry represents a unique dataset to test the ability of stellar population models to return robust photometric stellar mass ($M_*$) and star-formation rate (SFR) estimates. Here we use a spectroscopic sample of galaxies for which we possess $u g r i Z Y J H K_s$ ``gaussianized'' magnitudes from KiDS data release 4. We fit the spectral energy distribution from the 9-band photometry using: 1) three different popular libraries of stellar {population} templates, 2) single burst, simple and delayed exponential star-formation history models, and 3) a wide range of priors on age and metallicity. As template fitting codes we use two popular softwares: LePhare and CIGALE. We investigate the variance of the stellar masses and the star-formation rates from the different combinations of templates, star formation recipes and codes to assess the stability of these estimates and define some ``robust'' median quantities to be included in the upcoming KiDS data releases. As a science validation test, we derive the mass function, the star formation rate function, and the SFR-$M_*$ relation for a low-redshift ($z<0.5$) sample of galaxies, that result in excellent agreement with previous literature data. The final catalog, containing $\sim290\,000$ galaxies with redshift $0.01<z<0.9$, is made publicly available.

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