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Hot subdwarf star is a particular type of star that is crucial for studying binary evolution and atmospheric diffusion processes. In recent years, identifying Hot subdwarfs by machine learning methods has become a hot topic, but there are…
Hot subdwarf stars are core He burning stars located at the blue end of the horizontal branch, also known as the extreme horizontal branch. The properties of hot subdwarf stars are important for our understanding of the stellar…
Employing a new machine learning method, named hierarchical extreme learning machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data release (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope…
We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise…
Recent massive sky surveys in different bandwidths are providing new opportunities to modern astronomy. The Virtual Observatory (VO) represents the adequate framework to handle the huge amount of information available and filter out data…
Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for…
I develop a new technique to identify M-type extreme subdwarfs (esdMs) and demonstrate that it is substantially more efficient than previous methods. I begin by obtaining spectroscopy and improved photometry of a sample of 54 late-type halo…
Accurate classification of weather conditions in images is essential for enhancing the performance of object detection and classification models under varying weather conditions. This paper presents a comprehensive study on classifying…
A medium-resolution spectroscopic survey of helium-rich hot subdwarfs has been carried out using the Southern African Large Telescope (SALT). Objectives include the discovery of exotic hot subdwarfs, resolving distinct subclasses,…
A medium- and high-resolution spectroscopic survey of helium-rich hot subdwarfs is being carried out using the Southern African Large Telescope (SALT). Objectives include the discovery of exotic hot subdwarfs and of sequences connecting…
The estimation of the binary fraction of hot subdwarfs is key to shed light on the different evolution scenarios proposed to explain the loss of the hydrogen envelope during the red giant branch phase. In this paper we analyse the spectral…
Hot subdwarf stars are faint, blue objects, and are the main contributors to the far-UV excess observed in elliptical galaxies. They offer an excellent laboratory to study close and wide binary systems, and to scrutinize their interiors…
We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multi-systems, specifically focusing on double-line spectroscopic binary (SB2). Based on the ninth…
This study aims to optimize the few-shot image classification task and improve the model's feature extraction and classification performance by combining self-supervised learning with the deep network model ResNet-101. During the training…
We develop a template-fit method to automatically identify and classify late-type K and M dwarfs in spectra from the LAMOST. A search of the commissioning data, acquired in 2009-2010, yields the identification of 2612 late-K and M dwarfs.…
Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for…
Classification and probability estimation are fundamental tasks with broad applications across modern machine learning and data science, spanning fields such as biology, medicine, engineering, and computer science. Recent development of…
Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact…
To simplify the parameter of the deep learning network, a cascaded compressive sensing model "CSNet" is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the data. Secondly,…
Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…