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| 9Y-MST J0031-5521 , the SIMBAD biblio (32 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.18CEST03:11:37 |
| 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 |
|---|---|---|---|---|---|---|---|---|---|
|
2010ApJS..188..405A |
15 | D | 1 | 1513 | 938 | Fermi Large Area Telescope First Source Catalog. | ABDO A.A., ACKERMANN M., AJELLO M., et al. | ||
|
2012ApJS..199...31N |
15 | D | 1 | 2260 | 1282 | Fermi Large Area Telescope second source catalog. | NOLAN P.L., ABDO A.A., ACKERMANN M., et al. | ||
|
2012ApJ...753...83A |
15 | D | 1 | 669 | 88 | A statistical approach to recognizing source classes for unassociated sources in the first Fermi-LAT catalog. | ACKERMANN M., AJELLO M., ALLAFORT A., et al. | ||
|
2012MNRAS.424.2832L |
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. | ||
| 2012AJ....144..112F | 15 | D | 1 | 66 | 3 | Polarization and variation of near-infrared light from Fermi/LAT γ-ray sources. | FUJIWARA M., MATSUOKA Y. and IENAKA N. | ||
|
2013MNRAS.428..220H |
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..208...25T |
246 | D | X C | 6 | 141 | 18 | Multiband diagnostics of unidentified 1FGL sources with Suzaku and Swift X-ray observations. | TAKEUCHI Y., KATAOKA J., MAEDA K., et al. | |
|
2013ApJS..209....9P |
15 | D | 1 | 25 | 30 | Unveiling the nature of the unidentified gamma-ray sources. IV. The Swift catalog of potential X-ray counterparts. | PAGGI A., MASSARO F., D'ABRUSCO R., et al. | ||
|
2013ApJ...779..133A |
77 | C | 1 | 19 | 20 | Hunting for treasures among the Fermi unassociated sources: a multiwavelength approach. | ACERO F., DONATO D., OJHA R., et al. | ||
|
2014ApJ...782...41D |
15 | D | 2 | 235 | 15 | Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning. | DOERT M. and ERRANDO M. | ||
|
2015ApJS..217....2M |
16 | D | 3 | 2199 | 42 | Refining the associations of the Fermi large area telescope source catalogs. | MASSARO F., D'ABRUSCO R., LANDONI M., et al. | ||
|
2015ApJS..217....4S |
16 | D | 1 | 589 | 38 | New associations of gamma-ray sources from the Fermi second source catalog. | SCHINZEL F.K., PETROV L., TAYLOR G.B., et al. | ||
|
2015ApJS..218...23A |
16 | D | 1 | 3140 | 1411 | Fermi Large Area Telescope third source catalog. | ACERO F., ACKERMANN M., AJELLO M., et al. | ||
|
2016ApJ...820....8S |
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 |
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. | ||
|
2017ApJ...846...34A |
16 | D | 1 | 39 | 67 | The second catalog of flaring gamma-ray sources from the Fermi All-sky Variability Analysis. | ABDOLLAHI S., ACKERMANN M., AJELLO M., et al. | ||
|
2017MNRAS.470.1291S |
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 |
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. | ||
|
2018A&A...619A..23C |
16 | D | 1 | 1351 | ~ | A MST catalogue of γ-ray source candidates above 10 GeV and at Galactic latitudes higher than 20°. | CAMPANA R., MASSARO E. and BERNIERI E. | ||
|
2019ApJ...887..134K |
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 |
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. | ||
|
2020ApJS..247...33A |
16 | D | 1 | 5175 | 880 | Fermi Large Area Telescope fourth source catalog. | ABDOLLAHI S., ACERO F., ACKERMANN M., et al. | ||
|
2020ApJ...892..105A |
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. | ||
| 2020A&A...638A.128M | 16 | D | 1 | 51 | ~ | The γ-ray sky seen at X-ray energies. II. The Swift hunt of Fermi BL Lac objects among unidentified γ-ray sources. | MARCHESINI E.J., PAGGI A., 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. | ||
|
2022A&A...660A..87B |
17 | D | 2 | 6191 | 10 | Machine learning methods for constructing probabilistic Fermi-LAT catalogs. | BHAT A. and MALYSHEV D. | ||
|
2022ApJS..260...53A |
17 | D | 1 | 6710 | 133 | Incremental Fermi Large Area Telescope Fourth Source Catalog. | ABDOLLAHI S., ACERO F., BALDINI L., et al. | ||
|
2022ApJS..262...18Y |
17 | D | 1 | 2709 | 8 | The Spectral Energy Distributions for 4FGL Blazars. | YANG J.H., FAN J.H., LIU Y., et al. | ||
|
2022ApJS..263...24A |
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 |
17 | D | 4 | 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. | ||
|
2023ApJS..268...23F |
17 | D | 1 | 2708 | ~ | Characterizing the Emission Region Properties of Blazars. | FAN J., XIAO H., YANG W., 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. |