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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 (⁠|$\lt 0.3$|⁠ , i.e. a relatively extended SF history) prefer underdense 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. [ABSTRACT FROM AUTHOR]

The size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork 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. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, 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 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent 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 and feedback loops for optimization of Stage-IV survey observations. [ABSTRACT FROM AUTHOR]

The INSPIRE project has built the largest sample of ultra-compact massive galaxies (UCMGs) at 0.1 < z < 0.4 and obtained their star formation histories (SFHs). Due to their preserved very old stellar populations, relics are the perfect systems to constrain the earliest epochs of mass assembly in the Universe and the formation of massive early-type galaxies. The goal of this work is to investigate whether a correlation exists between the degree of relicness (DoR), quantifying the fraction of stellar mass formed at z > 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 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 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 larger dwarf-to-giant ratio, i.e. a bottom heavy IMF, than that of low-DoR counterparts. This might indicate that the cosmic epoch and therefore different formation scenarios influence the fragmentation of the star formation cloud and hence might be the explanation for IMF variations detected in massive ETGs. [ABSTRACT FROM AUTHOR]

The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we present lemon (Lens Modelling with Neural networks): a new machine-learning algorithm able to analyse hundreds of thousands of gravitational lenses in a reasonable amount of time. The algorithm is based on a Bayesian Neural Network : a new generation of neural networks able to associate a reliable confidence interval to each predicted parameter. We train the algorithm to predict the three main parameters of the singular isothermal ellipsoid model (the Einstein radius and the two components of the ellipticity) by employing two simulated data sets built to resemble the imaging capabilities of the Hubble Space Telescope and the forthcoming Euclid satellite. In this work, we assess the accuracy of the algorithm and the reliability of the estimated uncertainties by applying the network to several simulated data sets of 104 images each. We obtain accuracies comparable to previous studies present in the current literature and an average modelling time of just ∼0.5 s per lens. Finally, we apply the lemon algorithm to a pilot data set of real lenses observed with HST during the SLACS program, obtaining unbiased estimates of their SIE parameters. The code is publicly available on GitHub (https://github.com/fab-gentile/LEMON). [ABSTRACT FROM AUTHOR]

We explore the kinematic scaling relations of 38 dwarf galaxies in the Fornax Cluster using observations from the SAMI integral field spectrograph. We focus on the Fundamental Plane (FP), defined by the physical properties of the objects (scale length, surface brightness, and velocity dispersion) and the Stellar Mass (Fundamental) Plane, where surface brightness is replaced by stellar mass, and investigate their dynamical-to-stellar-mass ratio. We confirm earlier results that the Fornax dEs are significantly offset above the FP defined by massive, hot stellar systems. For the Stellar Mass (Fundamental) Plane, which shows much lower scatter, we find that young and old dwarf galaxies lie at about the same distance from the plane, all with comparable scatter. We introduce the perpendicular deviation of dwarf galaxies from the Stellar Mass Plane defined by giant early-types as a robust estimate of their DM fraction, and find that the faintest dwarfs are systematically offset above the plane, implying that they have a higher dark matter fraction. This result is confirmed when estimating the dynamical mass of our dEs using a virial mass estimator, tracing the onset of dark matter domination in low mass stellar systems. We find that the position of our galaxies on the Stellar Mass FP agrees with the galaxies in the Local Group. This seems to imply that the processes determining the position of dwarf galaxies on the FP depend on the environment in the same way, whether the galaxy is situated in the Local Group or in the Fornax Cluster. [ABSTRACT FROM AUTHOR]

Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved nature of dark matter (DM). At galaxy scales, the density distribution of DM is strongly affected by feedback processes, which are difficult to fully account for in classical techniques to derive galaxy masses. We explore the capability of supervised machine learning (ML) algorithms to predict the DM content of galaxies from 'luminous' observational-like parameters, using the TNG100 simulation. In particular, we use photometric (magnitudes in different bands), structural (the stellar half-mass radius and three different baryonic masses), and kinematic (1D velocity dispersion and the maximum rotation velocity) parameters to predict the total DM mass, DM half-mass radius, and DM mass inside one and two stellar half-mass radii. We adopt the coefficient of determination, R 2, as a metric to evaluate the accuracy of these predictions. We find that using all observational quantities together (photometry, structural, and kinematics), we reach high accuracy for all DM quantities (up to R 2 ∼ 0.98). This first test shows that ML tools are promising to predict the DM in real galaxies. The next steps will be to implement the observational realism of the training sets, by closely selecting samples that accurately reproduce the typical observed 'luminous' scaling relations. The so-trained pipelines will be suitable for real galaxy data collected from Rubin/Large Synoptic Survey Telescope (LSST), Euclid, Chinese Survey Space Telescope (CSST), 4-metre Multi-Object Spectrograph Telescope (4MOST), Dark Energy Spectroscopic Instrument (DESI), to derive e.g. the properties of their central DM fractions. [ABSTRACT FROM AUTHOR]

The star formation history (SFH) of galaxies is critical for understanding galaxy evolution. Hydrodynamical simulations enable us to precisely reconstruct the SFH of galaxies and establish a link to the underlying physical processes. In this work, we present a model to describe individual galaxies' SFHs from three simulations: TheThreeHundred , Illustris-1, and TNG100-1. This model divides the galaxy SFH into two distinct components: the 'main sequence' and the 'variation'. The 'main sequence' part is generated by tracing the history of the SFR − M * main sequence of galaxies across time. The 'variation' part consists of the scatter around the main sequence, which is reproduced by fractional Brownian motions. We find that: (1) the evolution of the main sequence varies between simulations; (2) fractional Brownian motions can reproduce many features of SFHs; however, discrepancies still exist; and (3) the variations and mass-loss rate are crucial for reconstructing the SFHs of the simulations. This model provides a fair description of the SFHs in simulations. On the other hand, by correlating the fractional Brownian motion model to simulation data, we provide a 'standard' against which to compare simulations. [ABSTRACT FROM AUTHOR]

We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using convolutional neural networks (CNNs). We train two different CNNs on composite images produced by superimposing simulated gravitational arcs on real Luminous Red Galaxies observed in VOICE. Specifically, the first CNN is trained on single-band images and more easily identifies systems with large Einstein radii, while the second one, trained on composite RGB images, is more accurate in retrieving systems with smaller Einstein radii. We apply both networks to real data from the VOICE survey, taking advantage of the high limiting magnitude (26.1 in the r band) and low PSF FWHM (0.8 arcsec in the r band) of this deep survey. We analyse ∼21 200 images with magr < 21.5, identifying 257 lens candidates. To retrieve a high-confidence sample and to assess the accuracy of our technique, nine of the authors perform a visual inspection. Roughly 75 per cent of the systems are classified as likely lenses by at least one of the authors. Finally, we assemble the LIVE sample (Lenses In VoicE) composed by the 16 systems passing the chosen grading threshold. Three of these candidates show likely lensing features when observed by the Hubble Space Telescope. This work represents a further confirmation of the ability of CNNs to inspect large samples of galaxies searching for gravitational lenses. These algorithms will be crucial to exploit the full scientific potential of forthcoming surveys with the Euclid satellite and the Vera Rubin Observatory. [ABSTRACT FROM AUTHOR]

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a major facility to carry out spectroscopic surveys for cosmology and galaxy evolution studies. The seventh data release of the LAMOST ExtraGAlactic Survey (LEGAS) is currently available and including redshifts of 193 361 galaxies. These sources are spread over |$\sim 11\, 500$| deg2 of the sky, largely overlapping with other imaging (SDSS and HSC) and spectroscopic (BOSS) surveys. The estimated depth of the galaxy sample, r ∼ 17.8, the high signal-to-noise ratio, and the spectral resolution R  = 1800, make the LAMOST spectra suitable for galaxy velocity dispersion (VD) measurements, which are invaluable to study the structure and formation of galaxies and to determine their central dark matter content. We present the first estimates of central VD of |$\sim 86\, 000$| galaxies in LAMOST footprint. We have used a wrap-up procedure to perform the spectral fitting using ppxf , and derive VD measurements. Statistical errors are also assessed by comparing LAMOST VD estimates with the ones of SDSS and BOSS over a common sample of |$\sim 51\, 000$| galaxies. The two data sets show a good agreement, within the statistical errors, in particular when VD values are corrected to 1 effective radius aperture. We also present a preliminary mass–σ relation and find consistency with previous analyses based on local galaxy samples. These first results suggest that LAMOST spectra are suitable for galaxy VD measurements to complement the available catalogues of galaxy internal kinematics in the Northern hemisphere. We plan to expand this analysis to next LAMOST data releases. [ABSTRACT FROM AUTHOR]

A catalogue of galaxy clusters was obtained in an area of 414 |$\deg ^2$| up to a redshift z ∼ 0.8 from the Data Release 3 of the Kilo-Degree Survey (KiDS-DR3), using the Adaptive Matched Identifier of Clustered Objects (amico) algorithm. The catalogue and the calibration of the richness–mass relation were presented in two companion papers. Here, we describe the selection of the cluster central galaxy and the classification of blue and red cluster members, and analyse the main cluster properties, such as the red/blue fraction, cluster mass, brightness, and stellar mass of the central galaxy, and their dependence on redshift and cluster richness. We use the Illustris-TNG simulation, which represents the state-of-the-art cosmological simulation of galaxy formation, as a benchmark for the interpretation of the results. A good agreement with simulations is found at low redshifts (z ≤ 0.4), while at higher redshifts the simulations indicate a lower fraction of blue galaxies than what found in the KiDS- amico catalogue: we argue that this may be due to an underestimate of star-forming galaxies in the simulations. The selection of clusters with a larger magnitude difference between the two brightest central galaxies, which may indicate a more relaxed cluster dynamical status, improves the agreement between the observed and simulated cluster mass and stellar mass of the central galaxy. We also find that at a given cluster mass the stellar mass of blue central galaxies is lower than that of the red ones. [ABSTRACT FROM AUTHOR]

We construct a suite of discrete chemo-dynamical models of the giant elliptical galaxy NGC 5846. These models are a powerful tool to constrain both the mass distribution and internal dynamics of multiple tracer populations. We use Jeans models to simultaneously fit stellar kinematics within the effective radius Re, planetary nebula (PN) radial velocities out to 3 Re, and globular cluster (GC) radial velocities and colours out to 6 Re. The best-fitting model is a cored dark matter halo which contributes ∼10 per cent of the total mass within 1 Re, and 67 per cent ± 10 per cent within 6 Re, although a cusped dark matter halo is also acceptable. The red GCs exhibit mild rotation with vmax/σ0 ∼ 0.3 in the region R > Re, aligned with but counter-rotating to the stars in the inner parts, while the blue GCs and PNe kinematics are consistent with no rotation. The red GCs are tangentially anisotropic, the blue GCs are mildly radially anisotropic, and the PNe vary from radially to tangentially anisotropic from the inner to the outer region. This is confirmed by general made-to-measure models. The tangential anisotropy of the red GCs in the inner regions could stem from the preferential destruction of red GCs on more radial orbits, while their outer tangential anisotropy – similar to the PNe in this region – has no good explanation. The mild radial anisotropy of the blue GCs is consistent with an accretion scenario. [ABSTRACT FROM AUTHOR]

We perform in-depth dynamical modelling of the luminous and dark matter (DM) content of the elliptical galaxy NGC 1407. Our strategy consists of solving the spherical Jeans equations for three independent dynamical tracers: stars, blue globular clusters (GCs) and red GCs in a self-consistent manner. We adopt a maximum-likelihood Markov Chain Monte Carlo fitting technique in the attempt to constrain the inner slope of the DM density profile (the cusp/core problem), and the stellar initial mass function (IMF) of the galaxy. We find the inner logarithmic slope of the DM density profiles to be γ = 0.6 ± 0.4, which is consistent with either a DM cusp (γ = 1) or with a DM core (γ = 0). Our findings are consistent with a Salpeter IMF, and marginally consistent with a Kroupa IMF. We infer tangential orbits for the blue GCs, and radial anisotropy for red GCs and stars. The modelling results are consistent with the virial mass-concentration relation predicted by A cold dark matter (CDM) simulations. The virial mass of NGC 1407 is logMvir = 13.3 ± 0.2M⊙, whereas the stellar mass is logM+ = 11.8 ± 0.1 M⊙. The overall uncertainties on the mass of NGC 1407 are only 5 per cent at the projected stellar effective radius. We attribute the disagreement between our results and previous X-ray results to the gas not being in hydrostatic equilibrium in the central regions of the galaxy. The halo of NGC 1407 is found be DM-dominated, with a dynamical mass-to-light ratio of M/L = 260+174-104 M⊙/L⊙.b. However, this value can be larger up to a factor of 3 depending on the assumed prior on the DM scale radius. [ABSTRACT FROM AUTHOR]

We examine correlations between masses, sizes and star formation histories for a large sample of low-redshift early-type galaxies, using a simple suite of dynamical and stellar population models. We confirm an anticorrelation between the size and stellar age and go on to survey for trends with the central content of dark matter (DM). An average relation between the central DM density and galaxy size of provides the first clear indication of cuspy DM haloes in these galaxies – akin to standard Λ cold dark matter haloes that have undergone adiabatic contraction. The DM density scales with galaxy mass as expected, deviating from suggestions of a universal halo profile for dwarf and late-type galaxies. We introduce a new fundamental constraint on galaxy formation by finding that the central DM fraction decreases with stellar age. This result is only partially explained by the size–age dependencies, and the residual trend is in the opposite direction to basic DM halo expectations. Therefore, we suggest that there may be a connection between age and halo contraction and that galaxies forming earlier had stronger baryonic feedback, which expanded their haloes, or lumpier baryonic accretion, which avoided halo contraction. An alternative explanation is a lighter initial mass function for older stellar populations. [ABSTRACT FROM AUTHOR]