Related papers: A Robust Hot Subdwarfs Identification Method Based…

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…

In this paper, we apply the feature-integration idea to fuse the abstract features extracted by Se-ResNet with experience features into hybrid features and input the hybrid features to the Support Vector Machine (SVM) to classify Hot…

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…

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…

Hot subdwarf stars are very important for understanding stellar evolution, stellar astrophysics, and binary star systems. Identifying more such stars can help us better understand their statistical distribution, properties, and evolution.…

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 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…

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…

This paper presents a new approach to classification of high dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches. The specific task we consider is identifying whether samples contain…

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,…

222 hot subdwarf stars were identified with LAMOST DR8 spectra, among which 131 stars show composite spectra and have been decomposed, while 91 stars present single-lined spectra. Atmospheric parameters of all sample stars were obtained by…

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…

We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies…

Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a dataset…

We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…

Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…

In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years…

In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a…

Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques…