Related papers: QSO Selection Algorithm Using Time Variability and…
We analyze the computational complexity of Quantum Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the $L_1$ norm of the feature weights vector and relies on a quantum linear programming solver instead…
Automatic classification of variability is now possible with tools like neural networks. Here, we present two neural networks for the identification of microlensing events -- the first discriminates against variable stars and the second…
We continue the QSO search in the 10 square degrees Schmidt field around M92 based on variability and proper motion (VPM) constraints. We have re-reduced 162 digitised B plates with a time-baseline of more than three decades and have…
We present variability and multi-wavelength photometric information for the 933 known quasars in the QUEST Variability Survey. These quasars are grouped into variable and non-variable populations based on measured variability confidence…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
Robust fast methods to classify variable light curves in large sky surveys are becoming increasingly important. While it is relatively straightforward to identify common periodic stars and particular transient events (supernovae, novae,…
Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data…
We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called Support Vector Machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument…
The MACHO project is a search for dark matter in the form of massive compact halo objects (MACHOs). The project has photometrically monitored tens of millions of stars in the Large Magellanic Cloud (LMC), Small Magellanic Cloud (SMC), and…
We present a catalog of 36,120 QSO candidates from the Galaxy Evolution Explorer (GALEX) Release Two (GR2) UV catalog and the USNO-A2.0 optical catalog. The selection criteria are established using known quasars from the Sloan Digital Sky…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
I present a new analysis of the MACHO Project 5.7 year Large Magellanic Cloud (LMC) microlensing data set that incorporates the effects of contamination of the microlensing event sample by variable stars. Photometric monitoring of MACHO LMC…
This paper aims at improving the classification accuracy of a Support Vector Machine (SVM) classifier with Sequential Minimal Optimization (SMO) training algorithm in order to properly classify failure and normal instances from oil and gas…
Classifying and summarizing large data sets from different sky survey projects is essential for various subsequent scientific research. By combining data from 4XMM-DR13, SDSS DR18, and CatWISE, we formed an XMM-WISE-SDSS sample that…
We present 97 QSO candidates in 48 Galactic Bulge fields of the Optical Gravitational Lensing Experiment II (OGLE-II) covering about 11 square degrees, which are selected via their variability. We extend light curves of variable objects…
We present a catalog of unobscured QSO candidates in the southern hemisphere from the early interim data of the KMTNet Synoptic Survey of Southern Sky (KS4). The KS4 data covers $\sim2500\,{\rm deg}^{2}$ sky area, reaching 5$\sigma$…
The selection of Gaussian kernel parameters plays an important role in the applications of support vector classification (SVC). A commonly used method is the k-fold cross validation with grid search (CV), which is extremely time-consuming…
Variable stars with well-calibrated period-luminosity relationships provide accurate distance measurements to nearby galaxies and are therefore a vital tool for cosmology and astrophysics. While these measurements typically rely on samples…
The study of absorptions along the lines of sight to bright high-$z$ QSOs is an invaluable cosmological tool that provides a wealth of information on the inter-/circum-galactic medium, Dark Matter, primordial elements, reionization,…
Accurate measurements of statistical properties, such as the star formation rate and the lifetime of young stellar objects (YSOs) in different stages, is essential for constraining star formation theories. However, it is a difficult task to…