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| 1RXS J013506.7+025558 , the SIMBAD biblio (17 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.19CEST08:34:54 |
| 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 |
|---|---|---|---|---|---|---|---|---|---|
|
2006A&A...449..425M |
15 | D | 1 | 2701 | 15 | Optical identification of ROSAT-FSC sources. | MICKAELIAN A.M., HOVHANNISYAN L.R., ENGELS D., et al. | ||
|
2017A&A...598A..17C |
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. | ||
|
2018MNRAS.474.1873W |
16 | D | 1 | 35327 | 23 | Spectral classification and composites of galaxies in LAMOST DR4. | WANG L.-L., LUO A.-L., SHEN S.-Y., et al. | ||
|
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. | ||
|
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. | ||
|
2020Ap.....63..166P |
16 | D | 1 | 199 | ~ | Activity types of galaxies selected from an HRC/BHRC sample. II. | PARONYAN G.M., MICKAELIAN A.M., ABRAHAMYAN H.V., et al. | ||
|
2021AJ....161..196P |
17 | D | 2 | 399 | 12 | An optical overview of blazars with LAMOST. I. Hunting changing-look blazars and new redshift estimates. | PENA-HERAZO H.A., MASSARO F., GU M., et al. | ||
|
2021ApJS..253...46P |
17 | D | 1 | 1078 | 38 | The central engines of Fermi blazars. | PALIYA V.S., DOMINGUEZ A., AJELLO M., 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 | 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.527.4763D | 18 | D | 2 | 353 | ~ | Constraints on redshifts of blazars from extragalactic background light attenuation using Fermi-LAT data. | DOMINGUEZ A., LAINEZ M., PALIYA V.S., et al. |