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 conducive to select quasar candidates. Two new color criterions (yW1W2 and izW1W2) are constructed to distinguish quasars from stars efficiently. With izW1W2, 98.30% of star contamination is eliminated, while 99.50% of quasars are retained, at least to the magnitude limit of our training set of stars. Based on the optical and infrared color features, we put forward an efficient schema to select quasar candidates and high redshift quasar candidates, in which two machine learning algorithms (XGBoost and SVM) are implemented. The XGBoost and SVM classifiers have proven to be very effective with accuracy of 99.46% when 8Color as input pattern and default model parameters. Applying the two optimal classifiers to the unknown Pan-STARRS and AllWISE cross-matched data set, a total of 2,006,632 intersected sources are predicted to be quasar candidates given quasar probability larger than 0.5 (i.e. P_QSO>0.5). Among them, 1,201,211 have high probability (P_QSO>0.95). For these newly predicted quasar candidates, a regressor is constructed to estimate their redshifts. Finally 7,402 z>3.5 quasars are obtained. Given the magnitude limitation and site of the LAMOST telescope, part of these candidates will be used as the input catalogue of the LAMOST telescope for follow-up observation, and the rest may be observed by other telescopes.
We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415…
Three-dimensional wide-field galaxy surveys are fundamental for cosmological studies. For higher redshifts (z > 1.0), where galaxies are too faint, quasars still trace the large-scale structure of the Universe. Since available telescope time limits spectroscopic surveys, photometric methods are efficient for estimating redshifts for many quasars. Recently, machine learning methods are increasingly successful for quasar photometric redshifts, however, they hinge on the distribution of the training set. Therefore a rigorous estimation of reliability is critical. We extracted optical and infrared…
A method is proposed for measuring the size of the broad emission line region (BLR) in quasars using broadband photometric data. A feasibility study, based on numerical simulations, points to the advantages and pitfalls associated with this approach. The method is applied to a subset of the Palomar-Green quasar sample for which independent BLR size measurements are available. An agreement is found between the results of the photometric method and the spectroscopic reverberation mapping technique. Implications for the measurement of BLR sizes and black hole masses for numerous quasars in the er…
Correlating BASS DR3 catalogue with ALLWISE database, the data from optical and infrared information are obtained. The quasars from SDSS are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost and Random forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to firstly classify the data into low- and high- redshift datase…
We apply one of lazy learning methods named k-nearest neighbor algorithm (kNN) to estimate the photometric redshifts of quasars, based on various datasets from the Sloan Digital Sky Survey (SDSS), UKIRT Infrared Deep Sky Survey (UKIDSS) and Wide-field Infrared Survey Explorer (WISE) (the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN arrives at the best performance when k is different with a special input pattern for a special dataset. The best resu…
We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by GA. This approach has two improvements compared to KNN: one is the feature weighted by GA; another is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors, i.e. $p\times z_{median} + (1-p)\times z_{mean}$. Based on the SDSS and SDSS-WISE quasar samples, we explore the performance of GeneticKNN for photometric redshift estimati…
Indian Institute of Science Education and Research, Bhopal（印度科学教育与研究学院博帕尔分院）；Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Pune Institute of Computer Technology（浦那计算机技术学院）；Millennium Institute of Astrophysics (MAS)（千年天体物理研究所（MAS））；Indian Institute of Astrophysics（印度天体物理研究所）；Inter University Centre for Astronomy and Astrophysics (IUCAA)（校际天文与天体物理中心（IUCAA））
We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising the need for human intervention. MargNet is the first classifier focusing e…
The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding…
Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and kNN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples depending on the redshift distribution. The performance based on d…
St. Thomas College（圣托马斯学院）；Inter-University Centre for Astronomy and Astrophysics（校际天文与天体物理中心）
We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour windo…
We quantitatively assess, by means of comprehensive numerical simulations, the ability of broad-band photometric surveys to recover the broad emission line region (BLR) size in quasars under various observing conditions and for a wide range of object properties. Focusing on the general characteristics of the Large Synoptic Survey Telescope (LSST), we find that the slope of the size-luminosity relation for the BLR in quasars can be determined with unprecedented accuracy, of order a few percent, over a broad luminosity range and out to $z\sim 3$. In particular, major emission lines for which the…
Polarized emission from a quasar is produced by wavelength-independent electron scattering surrounding its accretion disc, and thus avoid the contamination from its host galaxy and reveal the intrinsic emission spectrum of the accretion disc. Ultra-violet (UV) emission from a quasar is normally free from the contamination from its host galaxy. Polarization fraction of the quasar's disc emission can therefore be determined by comparing total UV emission with polarized visible to near-infrared (NIR) emission; and the resulting continuum spectrum from UV to infrared can reveal the theoretically e…
We apply a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. More…
Photometric classification of quasars from DES and photo-$z$ estimation with Machine Learning
University of Science and Technology of China（中国科学技术大学）；Instituto de Física, Universidade de São Paulo（圣保罗大学物理研究所）；Rhodes University（罗德岛大学）；Universidade Federal da Paraíba（帕拉伊巴联邦大学）；Secretaria de Estado da Ciência, Tecnologia, Inovação e Ensino Superior, Governo da Paraíba（帕拉伊巴州科学与技术、创新与高等教育厅）；Instituto Nacional de Pesquisas Espaciais（国家空间研究所）；Escola de Engenharia de Lorena (EEL), Universidade de São Paulo (USP)（洛雷纳工程学院，圣保罗大学）
Owing to the advent of large area photometric surveys, the possibility to use broad band photometric data, instead of spectra, to measure the size of the broad line region of active galactic nuclei, has raised a large interest. We describe here a new method using time-delay lensed quasars where one or several images are affected by microlensing due to stars in the lensing galaxy. Because microlensing decreases (or increases) the flux of the continuum compared to the broad line region, it changes the contrast between these two emission components. We show that this effect can be used to effecti…
Tabular foundation models for the estimation of probabilistic quasar photometric redshifts in S-PLUS
Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo（圣保罗大学大气科学、地球物理和天文研究所）；Observatório Nacional/MCTI（国家天文台/巴西科技部）；Departamento de Estatística, Universidade Federal de São Carlos（圣卡洛斯联邦大学统计系）；Cloudflare；Engenharia da Computação, Instituto Federal Fluminense, Campus Bom Jesus do Itabapoana（弗鲁米嫩塞联邦大学博热苏斯达伊塔巴波阿纳校区计算机工程系）；Escola Politécnica, Universidade de São Paulo（圣保罗大学理工学院）
