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| PSZ1 G287.95-32.98 , the SIMBAD biblio (10 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.16CEST16:31:08 |
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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 |
|---|---|---|---|---|---|---|---|---|---|
2014A&A...571A..29P 
|
15 | D | 1 | 1266 | 445 | Planck 2013 results. XXIX. The Planck catalogue of Sunyaev-Zeldovich sources. | PLANCK COLLABORATION, ADE P.A.R., AGHANIM N., et al. | ||
|
2015A&A...581A..14P
|
16 | D | 1 | 1234 | 91 | Planck 2013 results. XXXII. The updated Planck catalogue of Sunyaev-Zeldovich sources. | PLANCK COLLABORATION, ADE P.A.R., AGHANIM N., et al. | ||
|
2016A&A...594A..27P
|
16 | D | 1 | 1701 | 567 | Planck 2015 results. XXVII. The second Planck catalogue of Sunyaev-Zeldovich sources. | PLANCK COLLABORATION, ADE P.A.R., AGHANIM N., et al. | ||
|
2017MNRAS.468.1917R
|
16 | D | 1 | 245 | 69 | The cool-core state of Planck SZ-selected clusters versus X-ray-selected samples: evidence for cool-core bias. | ROSSETTI M., GASTALDELLO F., ECKERT D., et al. | ||
|
2017MNRAS.468.3322S
|
16 | D | 1 | 28164 | 37 | CoMaLit - V. Mass forecasting with proxies: method and application to weak lensing calibrated samples. | SERENO M. and ETTORI S. | ||
|
2019A&A...626A...7T
|
16 | D | 1 | 2353 | 1 | ComPRASS: a Combined Planck-RASS catalogue of X-ray-SZ clusters. | TARRIO P., MELIN J.-B. and ARNAUD M. | ||
|
2022MNRAS.513.3013Y
|
17 | D | 2 | 1644 | 7 | Dynamical state of galaxy clusters evaluated from X-ray images. | YUAN Z.S., HAN J.L. and WEN Z.L. | ||
|
2022ApJ...934..129S
|
17 | D | 1 | 439 | 14 | Combining Planck and SPT Cluster Catalogs: Cosmological Analysis and Impact on the Planck Scaling Relation Calibration. | SALVATI L., SARO A., BOCQUET S., et al. | ||
|
2024ApJS..272....7B
|
18 | D | 1 | 1653 | ~ | UPCluster-SZ: The Updated Catalog of Galaxy Clusters from the List of Planck Sunyaev-Zel'dovich Sources. | BAHK H. and HWANG H.S. | ||
| 2025MNRAS.538.2905M | 19 | D | 1 | 51 | ~ | Radio halo detection in MWA data using deep neural networks and generative data augmentation. | MISHRA A.K., TOLLEY E., KRISHNA S.P., et al. |
