3FGL J0919.5-2200 , the SIMBAD biblio

3FGL J0919.5-2200 , the SIMBAD biblio (20 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.16CEST17:52:00

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Bibcode/DOI Score in Title|Abstract|
Keywords 		in a table in teXt, Caption, ... Nb occurence Nb objects in ref Citations
(from ADS) 		Title First 3 Authors
2012ApJS..199...31N viz 15       D               1 2260 1282 Fermi Large Area Telescope second source catalog. NOLAN P.L., ABDO A.A., ACKERMANN M., et al.
2012MNRAS.424.2832L viz 15       D               1 1877 43 Application of the Gaussian mixture model in pulsar astronomy – pulsar classification and candidates ranking for the Fermi 2FGL catalogue. LEE K.J., GUILLEMOT L., YUE Y.L., et al.
2013MNRAS.428..220H viz 15       D               1 473 19 Gamma-ray active galactic nucleus type through machine-learning algorithms. HASSAN T., MIRABAL N., CONTRERAS J.L., et al.
2013ApJS..206...13M viz 15       D               1 380 49 Unveiling the nature of unidentified gamma-ray sources. II. Radio, infrared, and optical counterparts of the gamma-ray blazar candidates. MASSARO F., D'ABRUSCO R., PAGGI A., et al.
2014ApJS..213....3M viz 15       D               1 28362 27 The Low-frequency Radio Catalog of flat-spectrum sources. MASSARO F., GIROLETTI M., D'ABRUSCO R., et al.
2015ApJS..217....2M viz 16       D               2 2199 42 Refining the associations of the Fermi large area telescope source catalogs. MASSARO F., D'ABRUSCO R., LANDONI M., 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.
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.
2017A&A...602A..86L viz 16       D               2 1082 1 Research and characterisation of blazar candidates among the Fermi/LAT 3FGL catalogue using multivariate classifications. LEFAUCHEUR J. and PITA S.
2017MNRAS.470.1291S viz 16       D               1 563 2 3FGLzoo: classifying 3FGL unassociated Fermi-LAT γ-ray sources by artificial neural networks. SALVETTI D., CHIARO G., LA MURA G., et al.
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.
2021ApJ...908..177K 17       D               2 109 ~ Classifying blazar candidates from the 3FGL unassociated catalog into BL Lacertae objects and flat spectrum radio quasars Using Swift and WISE data. KAUR A., FALCONE A.D. and STROH M.C.
2021AJ....161..154K viz 17       D               2 185 ~ X-ray spectra and multiwavelength machine learning classification for likely counterparts to Fermi 3FGL unassociated sources. KERBY S., KAUR A., FALCONE A.D., et al.
2021MNRAS.505.5853G 17       D               1 5834 ~ Artificial Neural Network classification of 4FGL sources. GERMANI S., TOSTI G., LUBRANO P., 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.
2024MNRAS.530.2282D 1940 ~ Predicting conditional probability distributions of redshifts of Active Galactic Nuclei using Hierarchical Correlation Reconstruction. DUDA J. and BHATTA G.
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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