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| 1RXS J012203.6-300507 , the SIMBAD biblio (10 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.19CEST01:17:17 |
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More on score
| 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 |
|---|---|---|---|---|---|---|---|---|---|
2009ApJS..184..138H 
|
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. | ||
|
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. | ||
|
2019A&A...632A..77C
|
16 | D | 1 | 2017 | 54 | The 3HSP catalogue of extreme and high-synchrotron peaked blazars. | CHANG Y.-L., ARSIOLI B., GIOMMI P., et al. | ||
| 2021MNRAS.505.5853G | 17 | D | 1 | 5834 | ~ | Artificial Neural Network classification of 4FGL sources. | GERMANI S., TOSTI G., LUBRANO P., et al. | ||
|
2022A&A...660A..87B
|
17 | D | 1 | 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..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. | ||
| 2023MNRAS.523.4120D | 17 | D | 1 | 420 | ~ | Hybrid deep learning for blazar classification and correlation search with neutrinos. | DING J., HUANG Y., LI X.-D., 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. |
