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We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions…

Stellar astrophysics relies on diverse observational modalities-primarily photometric light curves and spectroscopic data from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within…

Large-scale spectroscopic surveys have collectively observed millions of stars across the Milky Way, but each derives stellar labels using independent pipelines with distinct modelling assumptions, introducing systematic offsets that…

The process of identifying stars is integral toward stellar based orientation determination in spacecraft. Star identification involves matching points in an image of the sky with stars in an astronomical catalog. A unified framework for…

Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we apply deep learning to microscopy, and demonstrate how neural networks can exploit the chromatic dependence of the…

Close binary stars are binary stars where the component stars are close enough such that they can exchange mass and/or energy. They are subdivided into semi-detached, overcontact or ellipsoidal binary stars. A challenging problem in the…

We present a classification of galaxies in the Pan-STARRS1 (PS1) 3$\pi$ survey based on their recent star formation history and morphology. Specifically, we train and test two Random Forest (RF) classifiers using photometric features…

Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral…

Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is…

We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution…

Along the life of the IUE project, a large archive with spectral data has been generated, requiring automated classification methods to be analyzed in an objective form. Previous automated classification methods used with IUE spectra were…

Achieving high accuracy and precision in stellar parameter and chemical composition determinations is challenging in massive star spectroscopy. On one hand, the target selection for an unbiased sample build-up is complicated by several…

We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for…

SDSS-V will obtain 100,000s of medium-resolution, optical spectra of M dwarfs with the BOSS instrument. M dwarfs have complex atmospheres, and their spectra contain many wide and dense, overlapping molecular features, so determining…

We present an expanded and improved deep-learning (DL) methodology for determining centers of star images on HST/WFPC2 exposures. Previously, we demonstrated that our DL model can eliminate the pixel-phase bias otherwise present in these…

Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…

Upcoming large-scale spectroscopic surveys such as WEAVE and 4MOST will provide thousands of spectra of massive stars, which need to be analysed in an efficient and homogeneous way. Studies on massive stars are usually based on samples of a…

We investigate the performance of some common machine learning techniques in identifying BHB stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from…

Because hyperspectral remote sensing images contain a lot of redundant information and the data structure is highly non-linear, leading to low classification accuracy of traditional machine learning methods. The latest research shows that…