1RXS J065201.0-480858 , the SIMBAD biblio

1RXS J065201.0-480858 , the SIMBAD biblio (32 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.18CEST23:46:37


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Title First 3 Authors
2009ApJS..184..138H viz 15       D               1 20623 68 XID II: statistical cross-association of ROSAT bright source catalog X-ray sources with 2MASS point source catalog near-infrared sources. HAAKONSEN C.B. and RUTLEDGE R.E.
2010MNRAS.401.1151M viz 15       D               1 3443 20 The RASS-6dFGS catalogue: a sample of X-ray selected AGN from the 6dF galaxy survey. MAHONY E.K., CROOM S.M., BOYLE B.J., et al.
2015ApJS..218...23A viz 16       D               1 3140 1411 Fermi Large Area Telescope third source catalog. ACERO F., ACKERMANN M., AJELLO M., et al.
2015ApJ...810...14A viz 16       D               1 1890 513 The third catalog of active galactic nuclei detected by the Fermi large area telescope. ACKERMANN M., AJELLO M., ATWOOD W.B., et al.
2016ApJ...820....8S viz 16       D               1 3047 91 Classification and ranking of Fermi LAT gamma-ray sources from the 3FGL catalog using machine learning techniques. SAZ PARKINSON P.M., XU H., YU P.L.H., et al.
2016ApJS..226...20F viz 16       D               1 1394 122 The spectral energy distributions of Fermi blazars. FAN J.H., YANG J.H., LIU Y., et al.
2016MNRAS.462.3180C viz 16       D               1 576 21 Blazar flaring patterns (B-FlaP) classifying blazar candidate of uncertain type in the third Fermi-LAT catalogue by artificial neural networks. CHIARO G., SALVETTI D., LA MURA G., et al.
2017A&A...598A..17C viz 16       D               1 1692 30 2WHSP: A multi-frequency selected catalogue of high energy and very high energy γ-ray blazars and blazar candidates. CHANG Y.-L., ARSIOLI B., GIOMMI P., et al.
2017A&A...602A..86L viz 16       D               1 1082 1 Research and characterisation of blazar candidates among the Fermi/LAT 3FGL catalogue using multivariate classifications. LEFAUCHEUR J. and PITA S.
2018ApJS..235...39C viz 16       D               1 1395 63 On the jet properties of γ-ray-loud active galactic nuclei. CHEN L.
2018ApJS..237...32A viz 16       D               1 2605 130 The search for spatial extension in high-latitude sources detected by the Fermi Large Area Telescope. ACKERMANN M., AJELLO M., BALDINI L., et al.
2018RAA....18...56K viz 16       D               1 1421 3 On the intrinsic shape of the gamma-ray spectrum for Fermi blazars. KANG S.-J., WU Q., ZHENG Y.-G., et al.
2019MNRAS.485...88P viz 16       D               1 1101 5 The Second LBA Calibrator Survey of southern compact extragalactic radio sources - LCS2. PETROV L., DE WITT A., SADLER E.M., et al.
2019ApJS..242....4D viz 16       D               1 9583 45 Two new catalogs of blazar candidates in the WISE Infrared sky. D'ABRUSCO R., ALVAREZ CRESPO N., MASSARO F., et al.
2019A&A...632A..77C viz 16       D               1 2017 54 The 3HSP catalogue of extreme and high-synchrotron peaked blazars. CHANG Y.-L., ARSIOLI B., GIOMMI P., et al.
2019MNRAS.490.4770K viz 16       D               1 567 ~ Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources. KOVACEVIC M., CHIARO G., CUTINI S., et al.
2019ApJ...887..134K viz 16       D               1 1314 ~ Evaluating the classification of Fermi BCUs from the 4FGL catalog using machine learning. KANG S.-J., LI E., OU W., et al.
2020MNRAS.493.1926K viz 16       D               1 1330 ~ Classification of blazar candidates of uncertain type from the Fermi LAT 8-yr source catalogue with an artificial neural network. KOVACEVIC M., CHIARO G., CUTINI S., et al.
2020ApJ...891...87K 16       D               1 120 ~ An empirical "high-confidence" candidate zone for Fermi BL Lacertae objects. KANG S.-J., ZHU K., FENG J., et al.
2020ApJS..247...33A viz 16       D               1 5175 880 Fermi Large Area Telescope fourth source catalog. ABDOLLAHI S., ACERO F., ACKERMANN M., et al.
2020ApJ...892..105A viz 16       D               1 3421 235 The fourth catalog of active galactic nuclei detected by the Fermi Large Area Telescope. AJELLO M., ANGIONI R., AXELSSON M., et al.
2020ApJS..248...23D viz 16       D               1 283 ~ On the physical association of Fermi-LAT blazars with their low-energy counterparts. DE MENEZES R., D'ABRUSCO R., MASSARO F., et al.
2021MNRAS.505.5853G 17       D               2 5834 ~ Artificial Neural Network classification of 4FGL sources. GERMANI S., TOSTI G., LUBRANO P., et al.
2021AJ....162..177P 17       D               6 64 5 Optical spectroscopic observations of gamma-ray blazar Candidates. XI. Optical observations from SOAR, blanco, NTT and OAN-SPM. The story so far. PENA-HERAZO H.A., PAGGI A., GARCIA-PEREZ A., et al.
2021A&A...655A..17S viz 17       D               1 338578 3 The SPECFIND V3.0 catalog of radio continuum cross-identifications and spectra: Reaching lower frequencies. STEIN Y., VOLLMER B., BOCH T., et al.
2022A&A...660A..87B viz 17       D               2 6191 10 Machine learning methods for constructing probabilistic Fermi-LAT catalogs. BHAT A. and MALYSHEV D.
2022ApJS..260...53A viz 17       D               1 6710 133 Incremental Fermi Large Area Telescope Fourth Source Catalog. ABDOLLAHI S., ACERO F., BALDINI L., et al.
2022ApJS..263...24A viz 17       D               1 3828 15 The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope: Data Release 3. AJELLO M., BALDINI L., BALLET J., et al.
2022ApJ...939..117Z viz 17       D               3 282 ~ New Identifications and Multiwavelength Properties of Extragalactic Fermi Gamma-Ray Sources in the SPT-SZ Survey Field. ZHANG L., VIEIRA J.D., AJELLO M., et al.
2024MNRAS.530.2282D 1940 ~ Predicting conditional probability distributions of redshifts of Active Galactic Nuclei using Hierarchical Correlation Reconstruction. DUDA J. and BHATTA G.
2024ApJ...967..104Z 18       D               3 372 ~ The Energy Budget in the Jet of High-frequency Peaked BL Lacertae Objects. ZHAO X.Z., YANG H.Y., ZHENG Y.G., et al.
2025PASP..137f4105D 19       D               1 290 ~ Improved Fermi Blazar Candidate Classifications with SRG/eROSITA X-Ray Counterparts Using Machine Learning. DING J. and RODRIGUEZ A.C.

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