Related papers: A Robust Hot Subdwarfs Identification Method Based…

We propose a revision of the system developed by L'epine et al. (2007) for spectroscopic M subdwarf classification. Based on an analysis of subdwarf spectra and templates from Savcheva et al. (2014), we show thatthe CaH1 feature originally…

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.…

182 single-lined hot subdwarf stars are identified by using spectra from the sixth and seventh data release (DR6 and DR7) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. We classified all the hot subdwarf…

We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference…

Convolutional Neutral Networks have been successfully applied in searching for strong lensing systems, leading to discoveries of new candidates from large surveys. On the other hand, systematic investigations about their robustness are…

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to…

We assembled a catalogue of bright, hot subdwarf and white dwarf stars extracted from a joint ultraviolet, optical, and infrared source list. The selection is secured using colour criteria that correlate well with effective temperatures…

Hot subdwarfs (hot sds) are compact, evolved stars near the Extreme Horizontal Branch (EHB) and are key to understanding stellar evolution and the ultraviolet excess in galaxies. We extend our previous analysis of Gaia XP spectra of hot…

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the…

Context. Hot subdwarfs, which are hot and small He-burning objects, are ideal targets for exploring the evolution of planetary systems after the red giant branch (RGB). Thus far, no planets have been confirmed around them, and no systematic…

M dwarfs are the most abundant stars in the Solar Neighborhood and they are prime targets for searching for rocky planets in habitable zones. Consequently, a detailed characterization of these stars is in demand. The spectral sub-type is…

Robustness of Deep Neural Networks (DNNs) is an important aspect to consider for their clinical applications. This work examined robustness issue for a DNN-based multi-class classification model via comprehensive experimental and simulation…

Recently, deep neural networks (DNNs) have been the subject of intense research for the classification of radio frequency (RF) signals, such as synthetic aperture radar (SAR) imagery or micro-Doppler signatures. However, a fundamental…

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is…

We present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo…

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model…

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…

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable…

With the advent of digital astronomy, new benefits and new problems have been presented to the modern day astronomer. While data can be captured in a more efficient and accurate manor using digital means, the efficiency of data retrieval…

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections…