ABSTRACT
In this work, we have implemented a detailed physical model of galaxy chemical enrichment into the Astraeus (seminumerical rAdiative tranSfer coupling of galaxy formaTion and Reionization in ...N-body dark matter simUlationS) framework which couples galaxy formation and reionization in the first billion years. Simulating galaxies spanning over 2.5 orders of magnitude in halo mass with $M_{\rm h} \sim 10^{8.9}{-}10^{11.5}\,{\rm M_\odot}$ ($M_{\rm h} \sim 10^{8.9}{-}10^{12.8}\rm M_\odot$) at z ∼ 10 (5), we find: (i) smooth accretion of metal-poor gas from the intergalactic medium (IGM) plays a key role in diluting the interstellar medium interstellar medium metallicity which is effectively restored due to self-enrichment from star formation; (ii) a redshift averaged gas-mass loading factor that depends on the stellar mass as $\eta _{\rm g} \approx 1.38 ({M_*}/{10^{10}\, {\rm \rm M_\odot }})^{-0.43}$; (iii) the mass–metallicity relation is already in place at z ∼ 10 and shows effectively no redshift evolution down to z ∼ 5; (iv) for a given stellar mass, the metallicity decreases with an increase in the star formation rate (SFR); (v) the key properties of the gas-phase metallicity (in units of 12 + log(O/H), stellar mass, SFR and redshift are linked through a high-redshift fundamental plane of metallicity (HFPZ) for which we provide a functional form; (vi) the mass–metallicity–SFR relations are effectively independent of the reionization radiative feedback model for $M_* {\,\, \buildrel\gt \over \sim \,\,}10^{6.5}\rm M_\odot$ galaxies; (vii) while low-mass galaxies ($M_{\rm h} {\,\, \buildrel\lt \over \sim \,\,}10^9\,\rm M_\odot$) are the key contributors to the metal budget of the IGM at early times, higher mass haloes provide about 50 per cent of the metal budget at lower redshifts.
Abstract
We report on the discovery of a new diffuse stellar substructure protruding for >5° from the northeastern rim of the LMC disk. The structure, which we dub the northeast structure (NES), was ...identified by applying a Gaussian mixture model to a sample of strictly selected candidate members of the Magellanic System, extracted from the Gaia EDR3 catalog. The NES fills the gap between the outer LMC disk and other known structures in the same region of the LMC, namely the northern tidal arm and the eastern substructures. Particularly noteworthy is that the NES is placed in a region where
N
-body simulations foresee a bending of the LMC disk due to tidal stresses induced by the MW. The velocity field in the plane of the sky indicates that the complex of tidal structures in the northeastern part of the LMC, including NES, shows a complex pattern. Additional data, as well as extensive dynamical modeling, is required to shed light onto the origin of NES as well as on the relationships with the surrounding substructures.
Aims.
In the era of large sky surveys, photometric redshifts (photo-
z
) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new machine learning (ML) ...tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only.
Methods.
As a first application of this tool, we estimate photo-
z
for a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on ∼140 000 galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG_AUTO < 21) and low-redshift (
z
< 0.8) systems; however, we could use ∼6500 galaxies in the range 0.8 <
z
< 3 to effectively extend the training to higher redshift. The inputs are the
r
-band galaxy images plus the nine-band magnitudes and colors from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey.
Results.
By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD = 0.014 for lower redshift and NMAD = 0.041 for higher redshift galaxies) and a low fraction of outliers (0.4% for lower and 1.27% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a ∼10%−35% improvement in precision at different redshifts and a ∼45% reduction in the fraction of outliers. We finally discuss the finding that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-
z
outlier fraction of galaxies can be cut down to 0.3%.
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 〈ρDM〉∝R−2eff 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.
Context.
The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) is aimed at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is ...the second paper of this series where we present a new, automatic object-classification method based on the machine learning technique.
Aims.
The main goal of this paper is to build a catalogue of bright extragalactic objects (galaxies and quasars) from the KiDS Data Release 4, with minimum stellar contamination and preserving the completeness as much as possible. We show here that this catalogue represents the perfect starting point to search for reliable gravitationally lensed quasar candidates.
Methods.
After testing some of the most used machine learning algorithms, decision-tree-based classifiers, we decided to use CatBoost, which was specifically trained with the aim of creating a sample of extragalactic sources that is as clean of stars as possible. We discuss the input data, define the training sample for the classifier, give quantitative estimates of its performances, and finally describe the validation results with
Gaia
DR2, AllWISE, and GAMA catalogues.
Results.
We built and made available to the scientific community the KiDS Bright EXtraGalactic Objects catalogue (KiDS-BEXGO), specifically created to find gravitational lenses but applicable to a wide number of scientific purposes. The KiDS-BEXGO catalogue is made of ≈6 million sources classified as quasars (≈200 000) and galaxies (≈5.7 M) up to
r
< 22
m
. To demonstrate the potential of the catalogue in the search for strongly lensed quasars, we selected ≈950 “Multiplets”: close pairs of quasars or galaxies surrounded by at least one quasar. We present cutouts and coordinates of the 12 most reliable gravitationally lensed quasar candidates. We showed that employing a machine learning method decreases the stellar contaminants within the gravitationally lensed candidates, comparing the current results to the previous ones, presented in the first paper from this series.
Conclusions.
Our work presents the first comprehensive identification of bright extragalactic objects in KiDS DR4 data, which is, for us, the first necessary step towards finding strong gravitational lenses in wide-sky photometric surveys, but has also many other more general astrophysical applications.
ABSTRACT
We use nine different galaxy formation scenarios in ten cosmological simulation boxes from the EAGLE (Evolution and Assembly of GaLaxies and their Environments) suite of Lambda cold dark ...matter hydrodynamical simulations to assess the impact of feedback mechanisms in galaxy formation and compare these to observed strong gravitational lenses. To compare observations with simulations, we create strong lenses with M* > 1011 M⊙ with the appropriate resolution and noise level, and model them with an elliptical power-law mass model to constrain their total mass density slope. We also obtain the mass–size relation of the simulated lens-galaxy sample. We find significant variation in the total mass density slope at the Einstein radius and in the projected stellar mass–size relation, mainly due to different implementations of stellar and active galactic nucleus (AGN) feedback. We find that for lens-selected galaxies, models with either too weak or too strong stellar and/or AGN feedback fail to explain the distribution of observed mass density slopes, with the counter-intuitive trend that increasing the feedback steepens the mass density slope around the Einstein radius (≈3–10 kpc). Models in which stellar feedback becomes inefficient at high gas densities, or weaker AGN feedback with a higher duty cycle, produce strong lenses with total mass density slopes close to isothermal i.e. −dlog (ρ)/dlog (r) ≈ 2.0 and slope distributions statistically agreeing with observed strong-lens galaxies in Sloan Lens ACS Survey and BOSS (Baryon Oscillation Spectroscopic Survey) Emission-Line Lens Survey. Agreement is only slightly worse with the more heterogeneous Strong Lensing Legacy Survey lens-galaxy sample. Observations of strong-lens-selected galaxies thus appear to favour models with relatively weak feedback in massive galaxies.
We investigated the stellar mass-metallicity relation (MZR) using a sample of 637 quiescent galaxies with $10.4 < 11.7$ selected from the LEGA-C survey at $0.6 z 1$. We derived mass-weighted stellar ...metallicities using full-spectral fitting. We find that while lower-mass galaxies are both metal-rich and metal-poor, there are no metal-poor galaxies at high masses, and that metallicity is bounded at low values by a mass-dependent lower limit. This lower limit increases with mass, empirically defining a MEtallicity-Mass Exclusion (MEME) zone. We find that the spectral index MgFe $ Mgb Fe4383 $, a proxy for the stellar metallicity, also shows a mass-dependent lower limit resembling the MEME relation. Crucially, MgFe is independent of stellar population models and fitting methods. By constructing the metallicity enrichment histories, we find that, after the first gigayear, the star formation history of galaxies has a mild impact on the observed metallicity distribution. Finally, from the average formation times, we find that galaxies populate differently the metallicity-mass plane at different cosmic times, and that the MEME limit is recovered by galaxies that formed at $z Our work suggests that the stellar metallicity of quiescent galaxies is bounded by a lower limit which increases with the stellar mass. On the other hand, low-mass galaxies can have metallicities as high as galaxies $ 1$ dex more massive. This suggests that, at $ 10.4$, rather than lower-mass galaxies being systematically less metallic, the observed MZR might be a consequence of the lack of massive metal-poor galaxies.
ABSTRACT
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, R2, 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 R2 ∼ 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
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.
Context. The galaxy total mass inside the effective radius is a proxy of the galaxy dark matter content and the star formation efficiency. As such, it encodes important information on the dark matter ...and baryonic physics. Aims. Total central masses can be inferred via galaxy dynamics or gravitational lensing, but these methods have limitations. We propose a novel approach based on machine learning to make predictions on total and dark matter content using simple observables from imaging and spectroscopic surveys. Methods. We used catalogs of multiband photometry, sizes, stellar mass, kinematic measurements (features), and dark matter (targets) of simulated galaxies from the Illustris-TNG100 hydrodynamical simulation to train a Mass Estimate machine Learning Algorithm (M ELA ) based on random forests. Results. We separated the simulated sample into passive early-type galaxies (ETGs), both normal and dwarf, and active late-type galaxies (LTGs) and showed that the mass estimator can accurately predict the galaxy dark masses inside the effective radius in all samples. We finally tested the mass estimator against the central mass estimates of a series of low-redshift ( z ≲ 0.1) datasets, including SPIDER, MaNGA/DynPop, and SAMI dwarf galaxies, derived with standard dynamical methods based on the Jeans equations. We find that M ELA predictions are fully consistent with the total dynamical mass of the real samples of ETGs, LTGs, and dwarf galaxies. Conclusions. M ELA learns from hydro-simulations how to predict the dark and total mass content of galaxies, provided that the real galaxy samples overlap with the training sample or show similar scaling relations in the feature and target parameter space. In this case, dynamical masses are reproduced within 0.30 dex (∼2 σ ), 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 M ELA a powerful alternative to predict the mass of galaxies of massive stage IV survey datasets using basic data, such as aperture photometry, stellar masses, fiber spectroscopy, and sizes. We finally discuss how to generalize these results to account for the variance of cosmological parameters and baryon physics using a more extensive variety of simulations and the further option of reverse engineering this approach and using model-free dark matter measurements (e.g., via strong lensing), plus visual observables, to predict the cosmology and the galaxy formation model.
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