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Register a new userIdentifying AGN host galaxies by Machine Learning with HSC+WISE
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We use machine learning techniques to investigate their performance in classifying active galactic nuclei (AGNs), including X-ray selected AGNs (XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs). Using known physical parameters in the Cosmic Evolution Survey (COSMOS) field, we are able to well-established training samples in the region of Hyper Suprime-Cam (HSC) survey. We compare several Python packages (e.g., scikit-learn, Keras, and XGBoost), and use XGBoost to identify AGNs and show the performance (e.g., accuracy, precision, recall, F1 score, and AUROC). Our results indicate that the performance is high for bright XAGN and IRAGN host galaxies. The combination of the HSC (optical) information with the Wide-field Infrared Survey Explorer (WISE) band-1 and WISE band-2 (near-infrared) information perform well to identify AGN hosts. For both type-1 (broad-line) XAGNs and type-1 (unobscured) IRAGNs, the performance is very good by using optical to infrared information. These results can apply to the five-band data from the wide regions of the HSC survey, and future all-sky surveys.
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We present a catalog of physical properties for galaxies hosting active galactic nuclei (AGN) detected by the Wide-field Infrared Survey Explorer (WISE). By fitting broadband spectral energy distributions of sources in the WISE AGN Catalog (Assef et al. 2018) with empirical galaxy and AGN templates, we derive photometric redshifts, AGN bolometric luminosities, measures of AGN obscuration, host galaxy stellar masses, and host galaxy star formation rates (SFRs) for 695,273 WISE AGN. The wide-area nature of this catalog significantly augments the known number of obscured AGN out to redshifts of z~3 and will be useful for studies focused on AGN or their host galaxy physical properties. We first show that the most likely non-AGN contaminants are galaxies at redshifts of z=0.2-0.3, with relatively blue W1-W2 colors, and with high specific SFRs for which the dust continuum emission is elevated in the W2 filter. Toward increasingly lower redshifts, WISE AGN host galaxies have systematically lower specific SFRs, relative to those of normal star forming galaxies, likely due to decreased cold gas fractions and the time delay between global star formation and AGN triggering. Finally, WISE AGN obscuration is not strongly correlated with AGN bolometric luminosity but shows a significant negative correlation with Eddington ratio. This result is consistent with a version of the `receding torus model in which the obscuring material is located within the supermassive black hole gravitational sphere of influence and the dust inner radius increases due to radiation pressure.
In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the Strong Gravitational Lenses Finding Challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc, without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up $sim$63 percent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of $77.25pm 0.48$% in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique.
We have measured the clustering of galaxies around active galactic nuclei (AGN) for which single-epoch virial masses of the super-massive black hole (SMBH) are available to investigate the relation between the large scale environment of AGNs and the evolution of SMBHs. The AGN samples used in this work were derived from the Sloan Digital Sky Survey (SDSS) observations and the galaxy samples were from 240~deg$^{2}$ S15b data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). The investigated redshift range is 0.6--3.0, and the masses of the SMBHs lie in the range $10^{7.5}$--$10^{10}$~M$_{odot}$. The absolute magnitude of the galaxy samples reaches to $M_{lambda 310}$ $sim$ $-$18 at rest frame wavelength 310~nm for the low-redshift end of the samples. More than 70% of the galaxies in the analysis are blue. We found a significant dependence of the cross-correlation length on redshift, which primarily reflects the brightness dependence of the galaxy clustering. At the lowest redshifts the cross-correlation length increases from 7~$h^{-1}$~Mpc around $M_{lambda 310}$$ = $$-19$~mag to $>$10~$h^{-1}$~Mpc beyond $M_{lambda 310}$$ = $$-20$~mag. No significant dependence of the cross-correlation length on BH mass was found for whole galaxy samples dominated by blue galaxies, while there was an indication of BH mass dependence in the cross-correlation with red galaxies. These results provides a picture of the environment of AGNs studied in this paper being enriched with blue starforming galaxies, and a fraction of the galaxies are being evolved to red galaxies along with the evolution of SMBHs in that system.
The Wide-field Infrared Survey Explorer (WISE) has scanned the entire sky with unprecedented sensitivity in four infrared bands, at 3.4, 4.6, 12, and 22 micron. The WISE Point Source Catalog contains more than 560 million objects, among them hundreds of thousands of galaxies with Active Nuclei (AGN). While type 1 AGN, owing to their bright and unobscured nature, are easy to detect and constitute a rather complete and unbiased sample, their type 2 counterparts, postulated by AGN unification, are not as straightforward to identify. Matching the WISE catalog with known QSOs in the Sloan Digital Sky Survey we confirm previous identification of the type 1 locus in the WISE color space. Using a very large database of the popular CLUMPY torus models, we find the colors of the putative type 2 counterparts, and also, for the first time, predict their number vs. flux relation that can be expected to be observed in any given WISE color range. This will allow us to put statistically very significant constraints on the torus parameters. Our results are a successful test of the AGN unification scheme.
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 the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network (DNN) with highway layers. This machine is trained by actual observed cadence and filter combinations such that we can directly input the observed data array into the machine without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.
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