English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Compact stellar systems such as Ultra-compact dwarfs (UCDs) and GlobularClusters (GCs) around galaxies are known to be the tracers of the merger eventsthat have been forming these galaxies. Therefore, identifying such systemsallows to study galaxies mass assembly, formation and evolution. However, inthe lack of spectroscopic information detecting UCDs/GCs using imaging data isvery uncertain. Here, we aim to train a machine learning model to separatethese objects from the foreground stars and background galaxies using themulti-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namelyu, g, r, i, J and Ks. The classes of objects are highly imbalanced which isproblematic for many automatic classification techniques. Hence, we employSynthetic Minority Over-sampling to handle the imbalance of the training data.Then, we compare two classifiers, namely Localized Generalized Matrix LearningVector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able toidentify UCDs/GCs with a precision and a recall of >93 percent and providerelevances that reflect the importance of each feature dimension %(colors andangular sizes) for the classification. Both methods detect angular sizes asimportant markers for this classification problem. While it is astronomicalexpectation 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, potentiallybecause of higher signal-to-noise ratio. Besides the excellent performance theLGMLVQ method allows further interpretability by providing the featureimportance for each individual class, class-wise representative samples and thepossibility for non-linear visualization of the data as demonstrated in thiscontribution. We conclude that employing machine learning techniques toidentify UCDs/GCs can lead to promising results.

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