Related papers: Identifying Evolutionary Stages of Molecular Clump…

Classification will be an important first step for upcoming surveys that will detect billions of new sources such as LSST and Euclid, as well as DESI, 4MOST and MOONS. The application of traditional methods of model fitting and…

We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic…

Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…

In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…

We have conducted a 3-mm molecular-line survey towards 570 high-mass star-forming clumps, using the Mopra telescope. The sample is selected from the 10,000 clumps identified by the ATLASGAL survey and includes all of the most important…

The chemical changes of high-mass star-forming regions provide a potential method for classifying their evolutionary stages and, ultimately, ages. In this study, we search for correlations between molecular abundances and the evolutionary…

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…

Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar coordinate transformation, we develop a rotationally…

Star formation (SF) studies are benefiting from the huge amount of data made available by recent large-area Galactic plane surveys conducted between 2 {\mu}m and 3 mm. Fully characterizing SF demands integrating far-infrared/sub-millimetre…

In the coming years, next-generation space-based infrared observatories will significantly increase our samples of rare massive stars, representing a tremendous opportunity to leverage modern statistical tools and methods to test massive…

Massive stars play an important role in the Universe. Unlike low-mass stars, the formation of these objects located at great distances is still unclear. It is expected to be governed by some combination of self-gravity, turbulence, and…

The environment plays a critical role in galaxy evolution, with galaxy clusters and their infall regions offering diverse conditions that shape galaxies before they enter the dense cluster core, a process known as ``pre-processing''.…

Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…

We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…

The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is…

Recent models and simulations of cluster formation within molecular clumps consider multi-scale, hierarchical accretion, which leads to clump mass growth over time. This mode of mass accumulation could have implications regarding the…

ATLASGAL is a 870-mircon dust survey of 420 square degrees of the inner Galactic plane and has been used to identify ~10 000 dense molecular clumps. Dedicated follow-up observations and complementary surveys are used to characterise the…

With high-resolution infrared data becoming available that can probe the formation of high-mass stellar clusters for the first time, models that make testable predictions of these objects are necessary. We utilize a three-dimensional…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…