Related papers: QSO Selection Algorithm Using Time Variability and…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining…
SDSS-V is carrying out a dedicated survey for white dwarfs, single and in binaries, and we report the analysis of the spectroscopy of 504 cataclysmic variables (CVs) and CV candidates obtained during the first 34 months of observations of…
Gravitationally lensed quasars are useful for studying astrophysics and cosmology, and enlarging the sample size of lensed quasars is important for multiple studies. In this work, we develop a lens search algorithm for four-image (quad)…
Quantum machine learning (QML) has witnessed immense progress recently, with quantum support vector machines (QSVMs) emerging as a promising model. This paper focuses on the two existing QSVM methods: quantum kernel SVM (QK-SVM) and quantum…
QSOs are essential for investigating the structure and evolution of the Universe. Historically, their identification has been concentrated in the northern hemisphere, primarily due to the sky coverage of major astronomical surveys. The…
Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the…
We consider linear random coefficient regression models, where the regressors are allowed to have a finite support. First, we investigate identifiability, and show that the means and the variances and covariances of the random coefficients…
Symbiotic stars are interacting binary systems, making them valuable for studying various astronomical phenomena, such as stellar evolution, mass transfer, and accretion processes. Despite recent progress in the discovery of symbiotic…
We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural…
AIMS. A recent catalogue by Flesch & Hardcastle presents two major anomalies in the spatial distribution of QSO candidates: i/ an apparent excess of such objects near bright galaxies, and ii/ an excess of very bright QSO candidates compared…
We present a lensed quasar search based on the variability of lens systems in the HSC transient survey. Starting from 101,353 variable objects with i-band photometry in the HSC transient survey, we used a variability-based lens search…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
We present photometric selection of type 1 quasars in the $\approx5.3~{\rm deg}^{2}$ XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically…
The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the…
We explore the multidimensional, multiwavelength selection of quasars from mid-IR (MIR) plus optical data, specifically from Spitzer-IRAC and the Sloan Digital Sky Survey (SDSS). We apply modern statistical techniques to combined Spitzer…
This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy…
Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive…
AIMS: We present a sample of candidate quasars selected using the KX-technique. The data cover 0.68 deg^2 of the X-ray Multi-Mirror (XMM) Large-Scale Structure (LSS) survey area where overlapping multi-wavelength imaging data permits an…