Abstract 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.
We present FORS2@VLT follow-up photometry of YMCA-1, a recently discovered stellar system located 13\degr~from the Large Magellanic Cloud (LMC) center. The deep color-magnitude diagram (CMD) reveals a well-defined main sequence (MS) and a handful of stars in the post-MS evolutionary phases. We analyse the YMCA-1 CMD by means of the automated isochrone matching package {\tt ASteCA} and model its radial density profile with a Plummer function. We find that YMCA-1 is an old ($11.7^{+1.7}_{-1.3}$~Gyr), metal-intermediate ([Fe/H] $\simeq -1.12^{+0.21}_{-0.13}$~dex), compact (r$_{\rm h} = 3.5 \pm 0.5$ pc), low-mass (M $= 10^{2.45 \pm 0.02} M_{\odot}$) and low-luminosity (M$_V = -0.47 \pm 0.57$~mag) stellar system. The estimated distance modulus ($\mu_0 = 18.72^{+0.15}_{-0.17}$~mag), corresponding to about 55~kpc, suggests that YMCA-1 is associated to the LMC, but we cannot discard the scenario in which it is a Milky Way satellite. The structural parameters of YMCA-1 are remarkably different compared with those of the 15 known old LMC globular clusters. In particular, it resides in a transition region of the M$_V$-r$_h$ plane, in between the ultra-faint dwarf galaxies and the classical old clusters, and close to SMASH-1, another faint stellar system recently discovered in the LMC surroundings.
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot , a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.
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.
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged)
Quadruply lensed quasars are extremely rare objects, but incredibly powerful cosmological tools. Only few dozen are known in the whole sky. Here we present the spectroscopic confirmation of two new quadruplets WG0214-2105 and WG2100-4452 discovered by Agnello & Spiniello (2018) within the Dark Energy Survey (DES) public footprints. We have conducted spectroscopic follow-up of these systems with the Southern African Large Telescope as part of a program that aims at confirming the largest possible number of optically selected strong gravitational lensing systems in the Equatorial and Southern Hemisphere. For both systems, we present the spectra for the sources and deflectors that allowed us to estimate the source redshifts and unambiguously confirm their lensing nature. For the brighter deflector (WG2100-4452), we measure the stellar velocity dispersion from the spectrum. We also obtain photometry for both lenses, directly from DES multi-band images, isolating the lens galaxies from the quasar images. One of the quadruplets, WG0214-2105, was also observed by Pan-STARRS, allowing us to estimate the apparent brightness of each quasar image at two different epochs, and thus to find evidence for flux variability. This result could suggest a microlensing event for the faintest components, although intrinsic variability cannot be excluded with only two epochs. Finally, we present simple lens models for both quadruplets, obtaining Einstein radii, SIE velocity dispersions, ellipticities, and position angles of the lens systems, as well as time delay predictions assuming a concordance cosmological model.
Abstract Modified Newtonian Dynamics (MOND) represents a phenomenological alternative to dark matter (DM) for the missing mass problem in galaxies and clusters of galaxies. We analyse the central regions of a local sample of ∼220 early-type galaxies from the ATLAS3D survey, to see if the data can be reproduced without recourse to DM. We estimate dynamical masses in the MOND context through Jeans analysis and compare to ATLAS3D stellar masses from stellar population synthesis. We find that the observed stellar mass–velocity dispersion relation is steeper than expected assuming MOND with a fixed stellar initial mass function (IMF) and a standard value for the acceleration parameter a0. Turning from the space of observables to model space (a) fixing the IMF, a universal value for a0 cannot be fitted, while, (b) fixing a0 and leaving the IMF free to vary, we find that it is ‘lighter’ (Chabrier like) for low-dispersion galaxies and ‘heavier’ (Salpeter like) for high dispersions. This MOND-based trend matches inferences from Newtonian dynamics with DM and from the detailed analysis of spectral absorption lines, adding to the converging lines of evidence for a systematically varying IMF.
We present measurements of the radial gravitational acceleration around isolated galaxies, comparing the expected gravitational acceleration given the baryonic matter with the observed gravitational acceleration, using weak lensing measurements from the fourth data release of the Kilo-Degree Survey. These measurements extend the radial acceleration relation (RAR) by 2 decades into the low-acceleration regime beyond the outskirts of the observable galaxy. We compare our RAR measurements to the predictions of two modified gravity (MG) theories: MOND and Verlinde's emergent gravity. We find that the measured RAR agrees well with the MG predictions. In addition, we find a difference of at least $6\sigma$ between the RARs of early- and late-type galaxies (split by S\'{e}rsic index and $u-r$ colour) with the same stellar mass. Current MG theories involve a gravity modification that is independent of other galaxy properties, which would be unable to explain this behaviour. The difference might be explained if only the early-type galaxies have significant ($M_{gas} \approx M_*$) circumgalactic gaseous haloes. The observed behaviour is also expected in $\Lambda$CDM models where the galaxy-to-halo mass relation depends on the galaxy formation history. We find that MICE, a $\Lambda$CDM simulation with hybrid halo occupation distribution modelling and abundance matching, reproduces the observed RAR but significantly differs from BAHAMAS, a hydrodynamical cosmological galaxy formation simulation. Our results are sensitive to the amount of circumgalactic gas; current observational constraints indicate that the resulting corrections are likely moderate. Measurements of the lensing RAR with future cosmological surveys will be able to further distinguish between MG and $\Lambda$CDM models if systematic uncertainties in the baryonic mass distribution around galaxies are reduced.
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