相关论文: QSO Selection Algorithm Using Time Variability and…
We present 663 QSO candidates in the Large Magellanic Cloud (LMC) selected using multiple diagnostics. We started with a set of 2,566 QSO candidates from our previous work selected using time variability of the MACHO LMC lightcurves. We…
A search for faint slowly variable objects was undertaken in the hope of finding QSO candidates behind the Small and Large Magellanic Clouds (SMC and LMC). This search used the optical variability properties of point sources from the…
Context. The identification of bright QSOs is of great importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in currently available photometric…
We develop and demonstrate a classification system constituted by several Support Vector Machines (SVM) classifiers, which can be applied to select quasar candidates from large sky survey projects, such as SDSS, UKIDSS, GALEX. How to…
The number and spatial distribution of confirmed quasi-stellar objects (QSOs) behind the Magellanic system is limited. This undermines their use as astrometric reference objects for different types of studies. We have searched for criteria…
Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly,…
We aim to select quasar candidates based on the two large survey databases, Pan-STARRS and AllWISE. Exploring the distribution of quasars and stars in the color spaces, we find that the combination of infrared and optical photometry is more…
Context. Lightcurve variability is well-suited for characterising objects in surveys with high cadence and long baseline. This is especially relevant in view of the large datasets to be produced by the Vera C. Rubin Observatory Legacy…
We present a new method of discovering galaxy-scale, strongly-lensed QSO systems from unresolved light curves using the autocorrelation function. The method is tested on five rungs of simulated light curves from the Time Delay Challenge 1…
In this work we train three decision-tree based ensemble machine learning algorithms (Random Forest Classifier, Adaptive Boosting and Gradient Boosting Decision Tree respectively) to study quasar selection in the variable source catalog in…
We present a method for the photometric selection of candidate quasars in multiband surveys. The method makes use of a priori knowledge derived from a subsample of spectroscopic confirmed QSOs to map the parameter space. The disentanglement…
Strong lensed quasi-stellar objects (QSOs) are valuable probes of the universe in numerous aspects. Two of these applications, reverberation mapping and measuring time delays for determining cosmological parameters, require the source QSOs…
We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves, having data points…
Quasi-stellar objects (QSOs) are a basis for an absolute reference system for astrometric studies. There is a need for creating such system behind nearby galaxies, to facilitate the measuring of the proper motions of these galaxies.…
We explored the AllWISE catalogue of the Wide-field Infrared Survey Explorer mission and identified Young Stellar Object candidates. Reliable 2MASS and WISE photometric data combined with Planck dust opacity values were used to build our…
Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and…
Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
We present the VST ATLAS Quasar Survey, consisting of $\sim1,229,000$ quasar (QSO) candidates with $16<g<22.5$ over $\sim4700$ deg$^2$. The catalogue is based on VST ATLAS$+$NEOWISE imaging surveys and aims to reach a QSO sky density of…
We have undertaken a dedicated program of automatic source classification in the WISE database merged with SuperCOSMOS scans, comprehensively identifying galaxies, quasars and stars on most of the unconfused sky. We use the Support Vector…