Relevant bibliographies by topics / WISE All-Sky catalogue

Academic literature on the topic 'WISE All-Sky catalogue'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "WISE All-Sky catalogue"

1

Shu, Yiping, Sergey E. Koposov, N. Wyn Evans, Vasily Belokurov, Richard G. McMahon, Matthew W. Auger, and Cameron A. Lemon. "Catalogues of active galactic nuclei from Gaia and unWISE data." Monthly Notices of the Royal Astronomical Society 489, no. 4 (September 5, 2019): 4741–59. http://dx.doi.org/10.1093/mnras/stz2487.

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ABSTRACT We present two catalogues of active galactic nucleus (AGN) candidates selected from the latest data of two all-sky surveys – Data Release 2 of the Gaia mission and the unWISE catalogue of the Wide-field Infrared Survey Explorer (WISE). We train a random forest classifier to predict the probability of each source in the Gaia–unWISE joint sample being an AGN, PRF, based on Gaia astrometric and photometric measurements and unWISE photometry. The two catalogues, which we designate C75 and R85, are constructed by applying different PRF threshold cuts to achieve an overall completeness of 75 per cent (≈90 per cent at GaiaG ≤ 20 mag) and reliability of 85 per cent, respectively. The C75 (R85) catalogue contains 2734 464 (2182 193) AGN candidates across the effective 36 000 deg2 sky, of which ≈0.91 (0.52) million are new discoveries. Photometric redshifts of the AGN candidates are derived by a random forest regressor using Gaia and WISE magnitudes and colours. The estimated overall photometric redshift accuracy is 0.11. Cross-matching the AGN candidates with a sample of known bright cluster galaxies, we identify a high-probability strongly lensed AGN candidate system, SDSS J1326+4806, with a large image separation of 21${^{\prime\prime}_{.}}$06. All the AGN candidates in our catalogues will have ∼5-yr long light curves from Gaia by the end of the mission, and thus will be a great resource for AGN variability studies. Our AGN catalogues will also be helpful in AGN target selections for future spectroscopic surveys, especially those in the Southern hemisphere. The C75 catalogue can be downloaded at https://www.ast.cam.ac.uk/~ypshu/AGN_Catalogues.html.
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Krakowski, T., K. Małek, M. Bilicki, A. Pollo, A. Kurcz, and M. Krupa. "Machine-learning identification of galaxies in the WISE × SuperCOSMOS all-sky catalogue." Astronomy & Astrophysics 596 (November 28, 2016): A39. http://dx.doi.org/10.1051/0004-6361/201629165.

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3

Wen, Z. L., J. L. Han, and F. Yang. "A catalogue of clusters of galaxies identified from all sky surveys of 2MASS, WISE, and SuperCOSMOS." Monthly Notices of the Royal Astronomical Society 475, no. 1 (December 9, 2017): 343–52. http://dx.doi.org/10.1093/mnras/stx3189.

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4

Zhang, Yanxia, Yongheng Zhao, and Xue-Bing Wu. "Classification of 4XMM-DR9 sources by machine learning." Monthly Notices of the Royal Astronomical Society 503, no. 4 (April 17, 2021): 5263–73. http://dx.doi.org/10.1093/mnras/stab744.

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ABSTRACT The ESA’s X-ray Multi-mirror Mission (XMM–Newton) created a new high-quality version of the XMM–Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is correlated with the Sloan Digital Sky Survey (SDSS) DR12 photometric data base and the AllWISE data base; we then get X-ray sources with information from the X-ray, optical, and/or infrared bands and obtain the XMM–WISE, XMM–SDSS, and XMM–WISE–SDSS samples. Based on the large spectroscopic surveys of SDSS and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), we cross-match the XMM–WISE–SDSS sample with sources of known spectral classes, and obtain known samples of stars, galaxies, and quasars. The distribution of stars, galaxies, and quasars as well as all spectral classes of stars in 2D parameter space is presented. Various machine-learning methods are applied to different samples from different bands. The better classified results are retained. For the sample from the X-ray band, a rotation-forest classifier performs the best. For the sample from the X-ray and infrared bands, a random-forest algorithm outperforms all other methods. For the samples from the X-ray, optical, and/or infrared bands, the LogitBoost classifier shows its superiority. Thus, all X-ray sources in the 4XMM-DR9 catalogue with different input patterns are classified by their respective models that are created by these best methods. Their membership of and membership probabilities for individual X-ray sources are assigned. The classified result will be of great value for the further research of X-ray sources in greater detail.
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5

Spiniello, C., and A. Agnello. "VEXAS: VISTA EXtension to Auxiliary Surveys." Astronomy & Astrophysics 630 (October 2019): A146. http://dx.doi.org/10.1051/0004-6361/201936311.

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Context. We present the first public data release of the VISTA EXtension to Auxiliary Surveys (VEXAS), comprising nine cross-matched multi-wavelength photometric catalogues where each object has a match in at least two surveys. Aims. Our aim is to provide spatial coverage that is as uniform as possible in the multi-wavelength sky and to provide the astronomical community with reference magnitudes and colours for various scientific uses: object classification (e.g. quasars, galaxies, and stars; high-z galaxies, white dwarfs); photometric redshifts of large galaxy samples; searches of exotic objects (e.g. extremely red objects and lensed quasars). Methods. We cross-matched the wide-field VISTA catalogues (the VISTA Hemisphere Survey and the VISTA Kilo Degree Infrared Galaxy Survey) with the AllWISE mid-infrared Survey, requiring a match within 10″. We have further matched this table with X-ray and radio data (ROSAT, XMM, SUMSS). We also performed a second cross-match between VISTA and AllWISE, with a smaller matching radius (3″), including WISE magnitudes. We then cross-matched this resulting table (≈138 × 106 objects) with three photometric wide-sky optical deep surveys (DES, SkyMapper, PanSTARRS). We finally included matches to objects with spectroscopic follow-up by the SDSS and 6dFGS. Results. To demonstrate the power of all-sky multi-wavelength cross-match tables, we show two examples of scientific applications of VEXAS, in particular using the publicly released tables to discover strong gravitational lenses (beyond the reach of previous searches) and to build a statistically large sample of extremely red objects. Conclusions. The VEXAS catalogue is currently the widest and deepest public optical-to-IR photometric and spectroscopic database in the southern hemisphere.
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6

Solarz, A., R. Thomas, F. M. Montenegro-Montes, M. Gromadzki, E. Donoso, M. Koprowski, L. Wyrzykowski, C. G. Diaz, E. Sani, and M. Bilicki. "Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey." Astronomy & Astrophysics 642 (October 2020): A103. http://dx.doi.org/10.1051/0004-6361/202038439.

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We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey. Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright (gAB < 19.5) objects marked as “anomalous” by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: (i) low redshift (z ∼ 0.03 − 0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; (ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; (iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources. To search for even more unusual sources, a more complete and balanced training set should be created after including these rare sub-species of otherwise abundant source classes, such as LoBALs. Such an iterative approach will ideally bring us closer to improving the strategy design for the detection of rarer sources contained within the vast data store of the AllWISE survey.
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7

Marton, G., P. Ábrahám, E. Szegedi-Elek, J. Varga, M. Kun, Á. Kóspál, E. Varga-Verebélyi, et al. "Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods." Monthly Notices of the Royal Astronomical Society 487, no. 2 (May 14, 2019): 2522–37. http://dx.doi.org/10.1093/mnras/stz1301.

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ABSTRACT The second Gaia Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean Gaia G magnitude &lt;20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the Gaia DR2 data base with Wide-field Infrared Survey Explorer (WISE) and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match table. We classified each object into four main classes: YSOs, extragalactic objects, main-sequence stars, and evolved stars. At a 90 per cent probability threshold, we identified 1 129 295 YSO candidates. To demonstrate the quality and potential of our YSO catalogue, here we present two applications of it. (1) We explore the 3D structure of the Orion A star-forming complex and show that the spatial distribution of the YSOs classified by our procedure is in agreement with recent results from the literature. (2) We use our catalogue to classify published Gaia Science Alerts. As Gaia measures the sources at multiple epochs, it can efficiently discover transient events, including sudden brightness changes of YSOs caused by dynamic processes of their circumstellar disc. However, in many cases the physical nature of the published alert sources are not known. A cross-check with our new catalogue shows that about 30 per cent more of the published Gaia alerts can most likely be attributed to YSO activity. The catalogue can be also useful to identify YSOs among future Gaia alerts.
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8

Bonjean, V., N. Aghanim, P. Salomé, A. Beelen, M. Douspis, and E. Soubrié. "Star formation rates and stellar masses from machine learning." Astronomy & Astrophysics 622 (February 2019): A137. http://dx.doi.org/10.1051/0004-6361/201833972.

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Star-formation activity is a key property to probe the structure formation and hence characterise the large-scale structures of the universe. This information can be deduced from the star formation rate (SFR) and the stellar mass (M⋆), both of which, but especially the SFR, are very complex to estimate. Determining these quantities from UV, optical, or IR luminosities relies on complex modeling and on priors on galaxy types. We propose a method based on the machine-learning algorithm Random Forest to estimate the SFR and the M⋆ of galaxies at redshifts in the range 0.01 < z < 0.3, independent of their type. The machine-learning algorithm takes as inputs the redshift, WISE luminosities, and WISE colours in near-IR, and is trained on spectra-extracted SFR and M⋆ from the SDSS MPA-JHU DR8 catalogue as outputs. We show that our algorithm can accurately estimate SFR and M⋆ with scatters of σSFR = 0.38 dex and σM⋆ = 0.16 dex for SFR and stellar mass, respectively, and that it is unbiased with respect to redshift or galaxy type. The full-sky coverage of the WISE satellite allows us to characterise the star-formation activity of all galaxies outside the Galactic mask with spectroscopic redshifts in the range 0.01 < z < 0.3. The method can also be applied to photometric-redshift catalogues, with best scatters of σSFR = 0.42 dex and σM⋆ = 0.24 dex obtained in the redshift range 0.1 < z < 0.3.
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9

De Rosa, R. J., B. Smith, J. Bulger, J. Patience, C. Marois, I. Song, B. Macintosh, J. Graham, R. Doyon, and M. Bessell. "Debris Disks and Multiplicity within the 75pc Volume-limited A-Star (VAST) Survey." Proceedings of the International Astronomical Union 8, S299 (June 2013): 334–35. http://dx.doi.org/10.1017/s174392131300882x.

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AbstractWe present the preliminary findings of an investigation of the multiplicity of debris disk stars identified within our Volume-limited A-star (VAST) multiplicity survey. Previous studies have produced conflicting results regarding the multiplicity fraction of debris disk-hosting stars compared with non-excess stars. By combining our large-scale volume-limited AO survey of A-type stars with the all-sky WISE catalogue, we have investigated the frequency of binary companions to a large sample of A-type stars with and without measured 22μm excess. The results of this study will allow for a greater understanding of the interaction between a companion star and a circumstellar debris disk, informing future study into the formation and stability of planetary-mass companions within binary systems.
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10

Ross, Nicholas P., and Nicholas J. G. Cross. "The near and mid-infrared photometric properties of known redshift z ≥ 5 quasars." Monthly Notices of the Royal Astronomical Society 494, no. 1 (March 13, 2020): 789–803. http://dx.doi.org/10.1093/mnras/staa544.

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ABSTRACT We assemble a catalogue of 488 spectroscopically confirmed very high (z ≥ 5.00) redshift quasars (VHzQ) and report their near- (ZYJHKs/K) and mid- (WISE W1234) infrared properties. 97 per cent of the VHzQ sample is detected in one or more near-infrared (NIR) band, with lack of coverage rather than lack of depth being the reason for the non-detections. 389 (80 per cent) of the very high redshift quasars are detected at 3.4 μm in the W1 band from the unWISE catalogue and all of the z ≥ 7 quasars are detected in both unWISE W1 and W2. Using archival Wide Field Camera (WFCAM)/United Kingdom Infrared Telescope (UKIRT) and VISTA Infrared Camera (VIRCAM)/Visible and Infrared Survey Telescope for Astronomy (VISTA) data we check for photometric variability that might be expected from super-Eddington accretion. We find 28 of the quasars have sufficient NIR measurements and signal-to-noise ratio to look for variability. Weak variability was detected in multiple bands of Sloan Digital Sky Survey (SDSS) J0959+0227, and very marginally in the Y-band of MMT J0215-0529. Only one quasar, SDSS J0349+0034, shows significant differences between WFCAM and VISTA magnitudes in one band. With supermassive black hole accretion likely to be redshift invariant up to very high redshift, further monitoring of these sources is warranted. All the data, analysis codes and plots used and generated here can be found at: github.com/d80b2t/VHzQ.
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