Identifying X-ray binary (XRB) candidates in nearby galaxies requires distinguishing them from possible contaminants including foreground stars and background active galactic nuclei. This work investigates the use of supervised machine learning algorithms to identify high-probability X-ray binary candidates. Using a catalogue of 943 Chandra X-ray sources in the Andromeda galaxy, we trained and tested several classification algorithms using the X-ray properties of 163 sources with previously known types. Amongst the algorithms tested, we find that random forest classifiers give the best performance and work better in a binary classification (XRB/non-XRB) context compared to the use of multiple classes. Evaluating our method by comparing with classifications from visible-light and hard X-ray observations as part of the Panchromatic Hubble Andromeda Treasury, we find compatibility at the 90% level, although we caution that the number of sources in common is rather small. The estimated probability that an object is an X-ray binary agrees well between the random forest binary and multiclass approaches and we find that the classifications with the highest confidence are in the X-ray binary class. The most discriminating X-ray bands for classification are the 1.7-2.8, 0.5-1.0, 2.0-4.0, and 2.0-7.0 keV photon flux ratios. Of the 780 unclassified sources in the Andromeda catalogue, we identify 16 new high-probability X-ray binary candidates and tabulate their properties for follow-up.
Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. We present random forest classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM, and modifying them to mimic future CEERS observations as well as nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the random forests using the merger history information for the simulated galaxies av…
The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation history. Previous surveys of the Lupus star-forming region have identified members through infrared excess and accretion signatures. We use machine learning to identify new candidate members of Lupus based on surveys from two space-based observatories: ESA's Gaia and NASA's Spitzer. Astrometric measurements from Gaia's Data Release 2 and astrometric and photom…
Open clusters (OCs) are regarded as tracers to understand stellar evolution theory and validate stellar models. In this study, we presented a robust approach to identifying OCs. A hybrid method of pyUPMASK and RF is first used to remove field stars and determine more reliable members. An identification model based on the RF algorithm built based on 3714 OC samples from Gaia DR2 and EDR3 is then applied to identify OC candidates. The OC candidates are obtained after isochrone fitting, the advanced stellar population synthesis (ASPS) model fitting, and visual inspection. Using the proposed appro…
Finding Quasars behind the Galactic Plane. IV. Candidate Selection from Chandra with Random Forest
Shenzhen Technology University（深圳理工大学）；National Astronomical Observatories（国家天文台）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）；Kapteyn Astronomical Institute, University of Groningen（格罗宁根大学卡普坦天文研究所）；Peking University（北京大学）
Determining the frequency and duration of changing--look (CL) active galactic nuclei (AGNs) phenomena, where the optical broad emission lines appear or disappear, is crucial to understand the evolution of the accretion flow around supermassive black holes. We present a strategy to select new CL candidates starting from a spectroscopic type 2 AGNs sample and searching for current type 1 photometric variability. We use the publicly available Zwicky Transient Facility (ZTF) alert stream and the Automatic Learning for the Rapid Classification of Events (ALeRCE) light curve classifier to produce a …
We present a method of selecting quasars up to redshift $\approx$ 6 with random forests, a supervised machine learning method, applied to Pan-STARRS1 and WISE data. We find that, thanks to the increasing set of known quasars we can assemble a training set that enables supervised machine learning algorithms to become a competitive alternative to other methods up to this redshift. We present a candidate set for the redshift range 4.8 to 6.3 which includes the region around z = 5.5 where quasars are difficult to select due to photometric similarity to red and brown dwarfs. We demonstrate that und…
Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. Methods. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopically-confirmed stars and quasars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colours. Exploiting NUV GALEX measurements for a subset of the…
The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54 r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the dataset is also particularly interesting in the context of performance forecasting for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). This work is the fifth in a series dedicated to the development of an automated, robust, and efficient methodology to identify optically variable AGN, aimed at deploying it on future LSST data. We test the perform…
Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's well-studied GC system to implement supervised machine learning (ML) classification algorithms - specifically random forest and neural networks - to identify GCs from foreground stars and background galaxies using ground-based photometry from the Canada-France-Hawai'i Telescope (CFHT). We compare these two ML classification methods to studies of "human-sele…
Tidal stripping and three-body interactions with the central supermassive black hole may eject stars from the Milky Way. These stars would comprise a set of `intragroup' stars that trace the past history of interactions in our galactic neighborhood. Using the Sloan Digital Sky Survey DR7, we identify candidate solar metallicity red giant intragroup stars using color cuts that are designed to exclude nearby M and L dwarfs. We present 677 intragroup candidates that are selected between 300 kpc and 2 Mpc, and are either the reddest intragroup candidates (M7-M10) or are L dwarfs at larger distance…
We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.3…
CURLING -- III. Identifying Candidates of Wide-separation Gravitationally Lensed Quasars from the CatNorth Catalogue
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Department of Physics, Nanchang University（南昌大学物理学院）；Center for Relativistic Astrophysics and High Energy Physics, Nanchang University（南昌大学相对论天体物理与高能物理中心）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）
Context. Quasars are spatially stationary, and they are essential objects in astrometry when defining reference frames. However, the census of quasars is far from complete. Mid-infared colors can be used to find quasar candidates because AGNs show a peculiar appearance in mid-infrared color, but these methods are incapable of separating quasars from AGNs. Aims. The aim of our study is to use astrometric and mid-infrared methods to select quasars and get a reliable quasar candidates catalog. Methods. We used a near-zero proper motion criterion in conjuction with WISE (all-sky Wide-field Infrare…
Ultra-compact high velocity clouds (UCHVCs) were identified in the ALFALFA HI survey as potential gas-bearing dark matter halos. Here we present higher resolution neutral hydrogen (HI) observations of twelve UCHVCS with the Westerbork Synthesis Radio Telescope (WSRT). The UCHVCs were selected based on a combination of size, isolation, large recessional velocity and high column density as the best candidate dark matter halos. The WSRT data were tapered to image the UCHVCs at 210" (comparable to Arecibo) and 105" angular resolution. In a comparison of the single-dish to interferometer data, we f…
Identifying Dwarfs of MC Analog GalaxiEs (ID-MAGE): The Search for Satellites Around Low-Mass Hosts
Dartmouth College（达特茅斯学院）；University of Arizona（亚利桑那大学）；Space Telescope Science Institute（太空望远镜科学研究所）；University of Tampa（坦帕大学）；University of Surrey（萨里大学）
We present a study of candidate galaxy-absorber pairs for 43 low redshift QSO sightlines ($0.06 < z < 0.85$) observed with the {\it Hubble Space Telescope}/Cosmic Origins Spectrograph that lie within the footprint of the Sloan Digital Sky Survey with a statistical approach to match absorbers with galaxies near the QSO lines of sight using only the SDSS Data Release 12 photometric data for the galaxies, including estimates of their redshifts. Our Bayesian methods combine the SDSS photometric information with measured properties of the circumgalactic medium to find the most probable galaxy match…
