Related papers: Identifying Evolutionary Stages of Molecular Clump…
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The…
Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic…
The intracluster light (ICL) is an important tracer of a galaxy cluster's history and past interactions. However, only small samples have been studied to date due to its very low surface brightness and the heavy manual involvement required…
Conventionally, CMD analyses of nearby star clusters are based on observations in 2 passbands. They are plagued by considerable degeneracies between age, metallicity, distance (and extinction) that can largely be resolved by including…
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 onset of massive star formation is not well understood because of observational and theoretical difficulties. To find the dense and cold clumps where massive star formation can take place, we compiled a sample of high infrared…
We present an enhanced unsupervised machine learning (UML) module within our previous \texttt{USmorph} classification framework featuring two components: (1) hierarchical feature extraction via a pre-trained ConvNeXt convolutional neural…
This paper studies the properties of kiloparsec-scale clumps in star-forming galaxies at z~2 through multi-wavelength broad band photometry. A sample of 40 clumps is identified through auto-detection and visual inspection from 10 galaxies…
The next generation near- and mid-infrared Galactic surveys will yield a large number of new highly obscured star clusters. Detailed characterization of these new objects with spectroscopy is time-consuming. Diagnostic tools that will be…
Understanding how stars form, evolve and impact molecular clouds is key to understanding why star formation is such an inefficient process globally. In this paper, we use the infrared bright fraction, $f_\text{IRB}$ (the fraction of a given…
Motivation: Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the…
Physical and chemical conditions (kinetic temperature, volume density, molecular composition) of interstellar clouds are inherent in their mm-submm line spectra, making spectral line profiles powerful diagnostics of source conditions. We…
The unprecedented volume and quality of data from space- and ground-based telescopes present an opportunity for machine learning to identify new classes of variable stars and peculiar systems that may have been overlooked by traditional…
In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount…
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation,…
In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into…
We report on a multi-wavelength (IR to cm) and multi-resolution (1 mas to 20 arcsec) exploration of high-mass star formation regions in the Galactic plane, at longitudes observable from the Southern Hemisphere. Our source sample was…
Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys. In this article, we investigate the classification of stars into spectral types using only the shape of their…
Selective Laser Melting (SLM) is a powder-bed additive manufacturing technique whose part quality depends critically on feedstock morphology. However, conventional powder characterization methods are low-throughput and qualitative, failing…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…