Understanding the Extreme Classes of Dwarf Galaxies

Most of the research on dwarf galaxies in the past has focused on the detailed properties of nearby systems or the most commonly occurring types of dwarfs. Alternatively, exceptional insight into these objects is gained through studying the most extreme examples. Therefore, this PhD research focuses on two extreme classes of dwarf galaxies, ultra-diffuse galaxies (UDGs) and ultra-compact dwarf galaxies (UCDs); UDGs as the most diffuse and UCDs as the most compact classes of dwarf galaxies. These two classes have raised several issues and open questions in the last years which challenge our understanding of dwarf galaxies, dark matter and in general, galaxy formation and evolution

Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques

Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to study galaxies mass assembly, formation and evolution. However, in the lack of spectroscopic information detecting UCDs/GCs using imaging data is very uncertain. Here, we aim to train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namely u, g, r, i, J and Ks. The classes of objects are highly imbalanced which is problematic for many automatic classification techniques. Hence, we employ Synthetic Minority Over-sampling to handle the imbalance of the training data. Then, we compare two classifiers, namely Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able to identify UCDs/GCs with a precision and a recall of >93% and provide relevances that reflect the importance of each feature dimension for the classification. Both methods detect angular sizes as important markers for this classification problem. While it is astronomical expectation that color indices of u−i and i−Ks are the most important colors, our analysis shows that colors such as g−r are more informative, potentially because of higher signal-to-noise ratio. Besides the excellent performance the LGMLVQ method allows further interpretability by providing the feature importance for each individual class, class-wise representative samples and the possibility for non-linear visualization of the data as demonstrated in this contribution. We conclude that employing machine learning techniques to identify UCDs/GCs can lead to promising results. Especially transparent methods allow further investigation and analysis of importance of the measurements for the detection problem and provide tools for non-linear visualization of the data

The number of globular clusters around the iconic UDG DF44 is as expected for dwarf galaxies

There is a growing consensus that the vast majority of ultra-diffuse galaxies (UDGs) are dwarf galaxies. However, there remain a few UDGs that seem to be special in terms of their globular cluster (GC) systems. In particular, according to some authors, certain UDGs exhibit large GC populations when compared to expectations from their stellar (or total) mass. Among these special UDGs, DF44 in the Coma cluster is one of the better-known examples. DF44 has been claimed to have a relatively high number of GCs, $N_{GC}=74^{+18}_{-18}$, for a stellar mass of only $3\times 10^8$ $M_{ \odot }$ which would indicate a much larger dark halo mass than dwarfs of similar stellar mass. In this paper we revisit this number and, contrary to previous results, find $N_{GC}=21^{+7}_{-9}$ assuming that the distribution of the GCs follows the same geometry as the galaxy. If we assume that the GCs around DF44 are distributed in a (projected) circularly symmetric way and, if we use a less strict criterion for the selection of the GCs, we find $N_{GC}=18^{+23}_{-12}$. Making use of the $M_{\rm GC} - M_{\rm halo}$ relation, this number of GCs suggests a dark matter halo mass of $M_{halo}=1.1^{+0.4}_{-0.5} \times 10^{11} M_{\odot}$, a value which is consistent with the expected total mass for DF44 based on its velocity dispersion, $\sigma=33^{+3}_{-3}$ km s$^{-1}$. We conclude that the number of GCs around DF44 is as expected for regular dwarf galaxies of similar stellar mass and DF44 is not extraordinary in this respect.Comment: 16 pages, 13 figures, accepted for publication in MNRAS, minor changes on the text to match the accepted versio

Mining archival data from wide-field astronomical surveys in search of near-Earth objects

Context. Increasing our knowledge of the orbits and compositions of near-earth objects (NEOs) is important for a better understanding of the evolution of the Solar System and life. The detection of serendipitous NEO appearances among the millions of archived exposures from large astronomical imaging surveys can provide a contribution which is complementary to NEO surveys. Aims. Using the ASTROWISE information system, this work aims to assess the detectability rate, the achieved recovery rate and the quality of astrometry when data mining the European Southern Observatory (ESO) archive for the OmegaCAM wide-field imager at the VLT Survey Telescope (VST). Methods. We developed an automatic pipeline that searches for NEO appearances inside the ASTROWISE environment. Throughout the recovery process the pipeline uses several public web tools (SSOIS, NEODyS, JPL Horizons) to identify possible images that overlap with the positions of NEOs, and acquires information on the NEOs’ predicted position and other properties (e.g. magnitude, rate, and direction of motion) at the time of observations. Considering these properties, the pipeline narrows down the search to potentially detectable NEOs, searches for streak-like objects across the images, and finds a matching streak for the NEOs. Results. We recovered 196 appearances of NEOs from a set of 968 appearances predicted to be recoverable. It includes appearances for three NEOs that were on the impact risk list at that point. These appearances occurred well before their discovery. The subsequent risk assessment using the extracted astrometry removes these NEOs from the risk list. More generally, we estimate a detectability rate of ~0.05 per NEO at a signal-to-noise ratio higher than 3 for NEOs in the OmegaCAM archive. Our automatic recovery rates are 40% and 20% for NEOs on the risk list and the full list, respectively. The achieved astrometric and photometric accuracy is on average 0.12″ and 0.1 mag. Conclusions. These results show the high potential of the archival imaging data of the ground-based wide-field surveys as useful instruments for the search, (p)recovery, and characterization of NEOs. Highly automated approaches, as possible using ASTROWISE, make this undertaking feasible.This work was executed as part of ESA contract no. 4000134667/21/D/MRP (CARMEN) with their Planetary Defence Office. The Big Data Layer of the Target Field Lab project “Mining Big Data” was used. The Target Field Lab is supported by the Northern Netherlands Alliance (SNN) and is financially supported by the European Regional Development Fund. The data science software system ASTROWISE runs on powerful databases and computing clusters at the Donald Smits Center of the University of Groningen and is supported, among other parties, by NOVA (the Dutch Research School for Astronomy). TSR acknowledges funding from the NEO-MAPP project (H2020-EU-2-1-6/870377). This work was (partially) supported by the Spanish MICIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” by the “European Union” through grant PID2021-122842OB-C21, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia “María de Maeztu”) through grant CEX2019-000918-M

Ultra-compact dwarfs beyond the centre of the Fornax galaxy cluster: hints of UCD formation in low-density environments

Ultra-compact dwarf galaxies (UCDs) were serendipitously discovered by spectroscopic surveys in the Fornax cluster 20 yr ago. Nowadays, it is commonly accepted that many bright UCDs are the nuclei of galaxies that have been stripped. However, this conclusion might be driven by biased samples of UCDs in high-density environments, on which most searches are based. With the deep optical images of the Fornax Deep Survey, combined with public near-infrared data, we revisit the UCD population of the Fornax cluster and search for UCD candidates, for the first time, systematically out to the virial radius of the galaxy cluster. Our search is complete down to magnitude m(g) = 21 mag or M-g similar to -10.5mag at the distance of the Fornax cluster. The UCD candidates are identified and separated from foreground stars and background galaxies by their optical and near-infrared colours. This primarily utilizes the u-i/i-Ks diagram and a machine learning technique is employed to incorporate other colour combinations to reduce the number of contaminants. The newly identified candidates (44) in addition to the spectroscopically confirmed UCDs (61), increase the number of known Fornax UCD considerably (105). Almost all of the new UCD candidates are located outside the Fornax cluster core (360 kpc), where all of the known UCDs were found. The distribution of UCDs within the Fornax cluster shows that a population of UCDs may form in low-density environments. This most likely challenges the current models of UCD formation

Implications for galaxy formation models from observations of globular clusters around ultradiffuse galaxies

We present an analysis of Hubble Space Telescope observations of globular clusters (GCs) in six ultradiffuse galaxies (UDGs) in the Coma cluster, a sample that represents UDGs with large effective radii (Re), and use the results to evaluate competing formation models. We eliminate two significant sources of systematic uncertainty in the determination of the number of GCs, NGC by using sufficiently deep observations that (i) reach the turnover of the globular cluster luminosity function (GCLF) and (ii) provide a sufficient number of GCs with which to measure the GC number radial distribution. We find that NGC for these galaxies is on average 20, which implies an average total mass, Mtotal, 1011 M when applying the relation between NGC and Mtotal. This value of NGC lies at the upper end of the range observed for dwarf galaxies of the same stellar mass and is roughly a factor of two larger than the mean. The GCLF, radial profile, and average colour are more consistent with those observed for dwarf galaxies than with those observed for the more massive (L∗) galaxies, while both the radial and azimuthal GC distributions closely follow those of the stars in the host galaxy. Finally, we discuss why our observations, specifically the GC number and GC distribution around these six UDGs, pose challenges for several of the currently favoured UDG formation models

Quasar Candidates behind the Milky Way Disk and M31

Quasars behind the Milky Way, M31 and other nearby galaxies are useful for absorption line studies. Here we report spectroscopic follow-up of four quasar candidates behind the Galactic plane and two behind the Andromeda galaxy

Ultra-compact dwarfs beyond the centre of the Fornax galaxy cluster:hints of UCD formation in low-density environments

Abstract Ultra-compact dwarf galaxies (UCDs) were serendipitously discovered by spectroscopic surveys in the Fornax cluster 20 yr ago. Nowadays, it is commonly accepted that many bright UCDs are the nuclei of galaxies that have been stripped. However, this conclusion might be driven by biased samples of UCDs in high-density environments, on which most searches are based. With the deep optical images of the Fornax Deep Survey, combined with public near-infrared data, we revisit the UCD population of the Fornax cluster and search for UCD candidates, for the first time, systematically out to the virial radius of the galaxy cluster. Our search is complete down to magnitude mg = 21 mag or Mg∼ −10.5 mag at the distance of the Fornax cluster. The UCD candidates are identified and separated from foreground stars and background galaxies by their optical and near-infrared colours. This primarily utilizes the u−i/i−Ks diagram and a machine learning technique is employed to incorporate other colour combinations to reduce the number of contaminants. The newly identified candidates (44) in addition to the spectroscopically confirmed UCDs (61), increase the number of known Fornax UCD considerably (105). Almost all of the new UCD candidates are located outside the Fornax cluster core (360 kpc), where all of the known UCDs were found. The distribution of UCDs within the Fornax cluster shows that a population of UCDs may form in low-density environments. This most likely challenges the current models of UCD formation