{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:30:11Z","timestamp":1768833011125,"version":"3.49.0"},"reference-count":84,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["PD\/BD\/150455\/2019"],"award-info":[{"award-number":["PD\/BD\/150455\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/FIS-AST\/29245\/2017"],"award-info":[{"award-number":["PTDC\/FIS-AST\/29245\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UID\/FIS\/04434\/2019"],"award-info":[{"award-number":["UID\/FIS\/04434\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04434\/2020"],"award-info":[{"award-number":["UIDB\/04434\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDP\/04434\/2020"],"award-info":[{"award-number":["UIDP\/04434\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["DL 57\/2016\/CP1364\/CT0002"],"award-info":[{"award-number":["DL 57\/2016\/CP1364\/CT0002"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Galaxies"],"abstract":"<jats:p>Active Galactic Nuclei (AGN) are relevant sources of radiation that might have helped reionising the Universe during its early epochs. The super-massive black holes (SMBHs) they host helped accreting material and emitting large amounts of energy into the medium. Recent studies have shown that, for epochs earlier than z\u223c5, the number density of SMBHs is on the order of few hundreds per square degree. Latest observations place this value below 300 SMBHs at z\u22736 for the full sky. To overcome this gap, it is necessary to detect large numbers of sources at the earliest epochs. Given the large areas needed to detect such quantities, using traditional redshift determination techniques\u2014spectroscopic and photometric redshift\u2014is no longer an efficient task. Machine Learning (ML) might help obtaining precise redshift for large samples in a fraction of the time used by other methods. We have developed and implemented an ML model which can predict redshift values for WISE-detected AGN in the HETDEX Spring Field. We obtained a median prediction error of \u03c3zN=1.48\u00d7(zPredicted\u2212zTrue)\/(1+zTrue)=0.1162 and an outlier fraction of \u03b7=11.58% at (zPredicted\u2212zTrue)\/(1+zTrue)&gt;0.15, in line with previous applications of ML to AGN. We also applied the model to data from the Stripe 82 area obtaining a prediction error of \u03c3zN=0.2501.<\/jats:p>","DOI":"10.3390\/galaxies9040086","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T22:14:52Z","timestamp":1635891292000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Exploring New Redshift Indicators for Radio-Powerful AGN"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0545-1113","authenticated-orcid":false,"given":"Rodrigo","family":"Carvajal","sequence":"first","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade de Lisboa, OAL, Tapada da Ajuda, PT1349-018 Lisbon, Portugal"},{"name":"Departamento de F\u00edsica, Faculdade de Ci\u00eancias, Universidade de Lisboa, Edif\u00edcio C8, Campo Grande, PT1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1177-3896","authenticated-orcid":false,"given":"Israel","family":"Matute","sequence":"additional","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade de Lisboa, OAL, Tapada da Ajuda, PT1349-018 Lisbon, Portugal"},{"name":"Departamento de F\u00edsica, Faculdade de Ci\u00eancias, Universidade de Lisboa, Edif\u00edcio C8, Campo Grande, PT1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-2973","authenticated-orcid":false,"given":"Jos\u00e9","family":"Afonso","sequence":"additional","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade de Lisboa, OAL, Tapada da Ajuda, PT1349-018 Lisbon, Portugal"},{"name":"Departamento de F\u00edsica, Faculdade de Ci\u00eancias, Universidade de Lisboa, Edif\u00edcio C8, Campo Grande, PT1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7948-5714","authenticated-orcid":false,"given":"Stergios","family":"Amarantidis","sequence":"additional","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade de Lisboa, OAL, Tapada da Ajuda, PT1349-018 Lisbon, Portugal"},{"name":"Departamento de F\u00edsica, Faculdade de Ci\u00eancias, Universidade de Lisboa, Edif\u00edcio C8, Campo Grande, PT1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2810-1375","authenticated-orcid":false,"given":"Davi","family":"Barbosa","sequence":"additional","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade de Lisboa, OAL, Tapada da Ajuda, PT1349-018 Lisbon, Portugal"},{"name":"Departamento de F\u00edsica, Faculdade de Ci\u00eancias, Universidade de Lisboa, Edif\u00edcio C8, Campo Grande, PT1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9454-859X","authenticated-orcid":false,"given":"Pedro","family":"Cunha","sequence":"additional","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal"},{"name":"Departamento de F\u00edsica e Astronomia, Faculdade de Ci\u00eancias, Universidade do Porto, Rua do Campo Alegre 687, PT4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0510-2351","authenticated-orcid":false,"given":"Andrew","family":"Humphrey","sequence":"additional","affiliation":[{"name":"Instituto de Astrof\u00edsica e Ci\u00eancias do Espa\u00e7o, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s00159-017-0102-9","article-title":"Active galactic nuclei: What\u2019s in a name?","volume":"25","author":"Padovani","year":"2017","journal-title":"Astron. Astrophys. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1146\/annurev-astro-081913-035722","article-title":"The Coevolution of Galaxies and Supermassive Black Holes: Insights from Surveys of the Contemporary Universe","volume":"52","author":"Heckman","year":"2014","journal-title":"Annu. Rev. Astron. Astrophys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1086\/507767","article-title":"Discovery of a z = 6.1 Radio-Loud Quasar in the NOAO Deep Wide Field Survey","volume":"652","author":"McGreer","year":"2006","journal-title":"Astrophys. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3847\/1538-4365\/abd483","article-title":"Giant Radio Quasars: Sample and Basic Properties","volume":"253","author":"Jamrozy","year":"2021","journal-title":"Astrophys. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1093\/mnras\/staa3837","article-title":"MIGHTEE: Are giant radio galaxies more common than we thought?","volume":"501","author":"Delhaize","year":"2021","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"126","DOI":"10.3847\/1538-4357\/ac042d","article-title":"The Discovery of a Remnant Radio Galaxy in A2065 Using GMRT","volume":"915","author":"Lal","year":"2021","journal-title":"Astrophys. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2694","DOI":"10.1093\/mnras\/stz551","article-title":"The first supermassive black holes: Indications from models for future observations","volume":"485","author":"Amarantidis","year":"2019","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1093\/mnras\/stab654","article-title":"The radio galaxy population in the SIMBA simulations","volume":"503","author":"Thomas","year":"2021","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1093\/mnras\/sty2603","article-title":"The Tiered Radio Extragalactic Continuum Simulation (T-RECS)","volume":"482","author":"Bonaldi","year":"2019","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Prandoni, I., and Seymour, N. (2014, January 9\u201313). Revealing the Physics and Evolution of Galaxies and Galaxy Clusters with SKA Continuum Surveys. Proceedings of the Advancing Astrophysics with the Square Kilometre Array (AASKA14), Giardini Naxos, Italy.","DOI":"10.22323\/1.215.0067"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1146\/annurev-astro-120419-014455","article-title":"The Assembly of the First Massive Black Holes","volume":"58","author":"Inayoshi","year":"2020","journal-title":"Annu. Rev. Astron. Astrophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1093\/mnras\/staa544","article-title":"The near and mid-infrared photometric properties of known redshift z \u2265 5 quasars","volume":"494","author":"Ross","year":"2020","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00159-007-0008-z","article-title":"Distant radio galaxies and their environments","volume":"15","author":"Miley","year":"2008","journal-title":"Astron. Astrophys. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1088\/0004-637X\/801\/1\/26","article-title":"The Last of FIRST: The Final Catalog and Source Identifications","volume":"801","author":"Helfand","year":"2015","journal-title":"Astrophys. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1071\/AS11021","article-title":"EMU: Evolutionary Map of the Universe","volume":"28","author":"Norris","year":"2011","journal-title":"Publ. Astron. Soc. Aust."},{"key":"ref_16","first-page":"175","article-title":"A Catalog of Very Large Array Sky Survey Epoch 1 Quick Look Components, Sources, and Host Identifications","volume":"4","author":"Gordon","year":"2020","journal-title":"Res. Notes Am. Astron. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1051\/0004-6361\/201833559","article-title":"The LOFAR Two-metre Sky Survey. II. First data release","volume":"622","author":"Shimwell","year":"2019","journal-title":"Astron. Astrophys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"A52","DOI":"10.1051\/0004-6361\/201423644","article-title":"Multiwavelength characterization of faint ultra steep spectrum radio sources: A search for high-redshift radio galaxies","volume":"569","author":"Singh","year":"2014","journal-title":"Astron. Astrophys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1093\/mnras\/sty026","article-title":"LOFAR-Bo\u00f6tes: Properties of high- and low-excitation radio galaxies at 0.5 < z < 2.0","volume":"475","author":"Williams","year":"2018","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"A107","DOI":"10.1051\/0004-6361\/202038671","article-title":"The LOFAR view of FR 0 radio galaxies","volume":"642","author":"Capetti","year":"2020","journal-title":"Astron. Astrophys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"A81","DOI":"10.1051\/0004-6361\/202039684","article-title":"Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4","volume":"649","author":"Nakoneczny","year":"2021","journal-title":"Astron. Astrophys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3847\/1538-3881\/ac0254","article-title":"Random Forests as a Viable Method to Select and Discover High-redshift Quasars","volume":"162","author":"Wenzl","year":"2021","journal-title":"Astron. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3847\/1538-4365\/aaf9a2","article-title":"A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best-Heckman Sample","volume":"240","author":"Ma","year":"2019","journal-title":"Astrophys. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1093\/mnras\/stz1289","article-title":"Morphological classification of radio galaxies: Capsule networks versus convolutional neural networks","volume":"487","author":"Lukic","year":"2019","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"A89","DOI":"10.1051\/0004-6361\/202038500","article-title":"Unveiling the rarest morphologies of the LOFAR Two-metre Sky Survey radio source population with self-organised maps","volume":"645","author":"Mostert","year":"2021","journal-title":"Astron. Astrophys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"A102","DOI":"10.1051\/0004-6361\/202039488","article-title":"FR-type radio sources at 3 GHz VLA-COSMOS: Relation to physical properties and large-scale environment","volume":"648","author":"Vardoulaki","year":"2021","journal-title":"Astron. Astrophys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.1093\/mnras\/stab1545","article-title":"Light-curve classification with recurrent neural networks for GOTO: Dealing with imbalanced data","volume":"505","author":"Burhanudin","year":"2021","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3847\/0004-637X\/820\/1\/8","article-title":"Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques","volume":"820","author":"Xu","year":"2016","journal-title":"Astrophys. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3180","DOI":"10.1093\/mnras\/stw1830","article-title":"Blazar flaring patterns (B-FlaP) classifying blazar candidate of uncertain type in the third Fermi-LAT catalogue by artificial neural networks","volume":"462","author":"Chiaro","year":"2016","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100387","DOI":"10.1016\/j.ascom.2020.100387","article-title":"Efficient Fermi source identification with machine learning methods","volume":"32","author":"Xiao","year":"2020","journal-title":"Astron. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, C., Bai, Y., L\u00f3pez-Sanjuan, C., Yuan, H., Wang, S., Liu, J., Sobral, D., Baqui, P.O., Mart\u00edn, E.L., and Galarza, C.A. (2021). J-PLUS: Support Vector Machine Applied to STAR-GALAXY-QSOClassification. arXiv.","DOI":"10.1051\/0004-6361\/202142254"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e2022038118","DOI":"10.1073\/pnas.2022038118","article-title":"AI-assisted superresolution cosmological simulations","volume":"118","author":"Li","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1142\/S0218271810017160","article-title":"Data Mining and Machine Learning in Astronomy","volume":"19","author":"Ball","year":"2010","journal-title":"Int. J. Mod. Phys. D"},{"key":"ref_34","unstructured":"Baron, D. (2019). Machine Learning in Astronomy: A practical overview. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., and Holzinger, A. (2018, January 27\u201330). Explainable ai: The new 42?. Proceedings of the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Hamburg, Germany.","DOI":"10.1007\/978-3-319-99740-7_21"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"42200","DOI":"10.1109\/ACCESS.2020.2976199","article-title":"Explainable Machine Learning for Scientific Insights and Discoveries","volume":"8","author":"Roscher","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","first-page":"307","article-title":"A Value for n-Person Games","volume":"Volume 1","author":"Shapley","year":"1953","journal-title":"Contributions to the Theory of Games (AM-28), Volume II"},{"key":"ref_38","unstructured":"Molnar, C. (2021, May 04). Interpretable Machine Learning. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3847\/1538-4365\/abd805","article-title":"The CatWISE2020 Catalog","volume":"253","author":"Marocco","year":"2021","journal-title":"Astrophys. J."},{"key":"ref_40","unstructured":"Fernique, P., Boch, T., Donaldson, T., Durand, D., O\u2019Mullane, W., Reinecke, M., and Taylor, M. (2015). MOC\u2014HEALPix Multi-Order Coverage map Version 1.0. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3847\/1538-4365\/abb82d","article-title":"The Pan-STARRS1 Database and Data Products","volume":"251","author":"Flewelling","year":"2020","journal-title":"Astrophys. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3847\/1538-4365\/aa7053","article-title":"Revised Catalog of GALEX Ultraviolet Sources. I. The All-Sky Survey: GUVcat_AIS","volume":"230","author":"Bianchi","year":"2017","journal-title":"Astrophys. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"A78","DOI":"10.1051\/0004-6361\/201628536","article-title":"The GMRT 150 MHz all-sky radio survey. First alternative data release TGSS ADR1","volume":"598","author":"Intema","year":"2017","journal-title":"Astron. Astrophys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"A137","DOI":"10.1051\/0004-6361\/202037706","article-title":"The XMM-Newton serendipitous survey. X. The second source catalogue from overlapping XMM-Newton observations and its long-term variable content","volume":"641","author":"Traulsen","year":"2020","journal-title":"Astron. Astrophys."},{"key":"ref_45","unstructured":"Cutri, R.M., Skrutskie, M.F., van Dyk, S., Beichman, C.A., Carpenter, J.M., Chester, T., Cambresy, L., Evans, T., Fowler, J., and Gizis, J. (2021, May 29). 2MASS All Sky Catalog of Point Sources. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2003tmc..book.....C\/abstract."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1086\/498708","article-title":"The Two Micron All Sky Survey (2MASS)","volume":"131","author":"Skrutskie","year":"2006","journal-title":"Astron. J."},{"key":"ref_47","unstructured":"Cutri, R.M., Wright, E.L., Conrow, T., Fowler, J.W., Eisenhardt, P.R.M., Grillmair, C., Kirkpatrick, J.D., Masci, F., McCallon, H.L., and Wheelock, S.L. (2021, May 29). Explanatory Supplement to the AllWISE Data Release Products. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2013wise.rept....1C."},{"key":"ref_48","unstructured":"Flesch, E.W. (2021). The Million Quasars (Milliquas) v7.2 Catalogue, now with VLASS associations. The inclusion of SDSS-DR16Q quasars is detailed. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3847\/1538-4365\/aba623","article-title":"The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release","volume":"250","author":"Lyke","year":"2020","journal-title":"Astrophys. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"A97","DOI":"10.1051\/0004-6361\/201833103","article-title":"Return of the features. Efficient feature selection and interpretation for photometric redshifts","volume":"616","author":"Cavuoti","year":"2018","journal-title":"Astron. Astrophys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"A31","DOI":"10.1051\/0004-6361\/201014885","article-title":"PHAT: PHoto-z Accuracy Testing","volume":"523","author":"Hildebrandt","year":"2010","journal-title":"Astron. Astrophys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1111\/j.1365-2966.2009.15748.x","article-title":"Catastrophic photometric redshift errors: Weak-lensing survey requirements","volume":"401","author":"Bernstein","year":"2010","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4847","DOI":"10.1093\/mnras\/stab1513","article-title":"Benchmarking and scalability of machine-learning methods for photometric redshift estimation","volume":"505","author":"Henghes","year":"2021","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3389\/fspas.2021.658229","article-title":"Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case","volume":"8","author":"Brescia","year":"2021","journal-title":"Front. Astron. Space Sci."},{"key":"ref_55","unstructured":"Ali, M. (2021, October 23). PyCaret: An Open Source, Low-Code Machine Learning Library in Python. PyCaret Version 2.3. Available online: https:\/\/www.pycaret.org."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, A.K. (2017). Incomplete Data in Astrostatistics. Wiley StatsRef: Statistics Reference Online, American Cancer Society.","DOI":"10.1002\/9781118445112.stat07942"},{"key":"ref_57","unstructured":"Bilogur, A., Beutner, V., Fandango, A., Everson, B., Chacreton, D., Abahurire, E.J., Mavroforakis, H., Cruz, J.S., and Mahlke, M. (2021). ResidentMario\/missingno: 0.5.0 maintenance release. Zenodo."},{"key":"ref_58","first-page":"1","article-title":"Feature Selection with the Boruta Package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw. Artic."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","article-title":"A new family of power transformations to improve normality or symmetry","volume":"87","author":"Yeo","year":"2000","journal-title":"Biometrika"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_61","unstructured":"Dorogush, A.V., Gulin, A., Gusev, G., Kazeev, N., Prokhorenkova, L.O., and Vorobev, A. (2017). Fighting biases with dynamic boosting. arXiv."},{"key":"ref_62","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv."},{"key":"ref_63","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the KDD \u201916: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3847\/1538-4357\/aa937d","article-title":"AGN Populations in Large-volume X-Ray Surveys: Photometric Redshifts and Population Types Found in the Stripe 82X Survey","volume":"850","author":"Ananna","year":"2017","journal-title":"Astrophys. J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1088\/0004-6256\/142\/1\/3","article-title":"High-resolution Very Large Array Imaging of Sloan Digital Sky Survey Stripe 82 at 1.4 GHz","volume":"142","author":"Hodge","year":"2011","journal-title":"Astron. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1093\/mnras\/stab485","article-title":"QSO photometric redshifts using machine learning and neural networks","volume":"503","author":"Curran","year":"2021","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_69","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2522","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1093\/mnras\/staa3067","article-title":"RAiSERed: Radio continuum redshifts for lobed active galactic nuclei","volume":"499","author":"Turner","year":"2020","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"A83","DOI":"10.1051\/0004-6361\/201833388","article-title":"Radio spectral index distribution of SDSS-FIRST sources across optical diagnostic diagrams","volume":"630","author":"Busch","year":"2019","journal-title":"Astron. Astrophys."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1111\/j.1365-2966.2008.13806.x","article-title":"On the origin of radio emission in radio-quiet quasars","volume":"390","author":"Laor","year":"2008","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5513","DOI":"10.1093\/mnras\/sty3098","article-title":"What drives the radio slopes in radio-quiet quasars?","volume":"482","author":"Laor","year":"2019","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"L36","DOI":"10.1093\/mnrasl\/slab033","article-title":"Gravitational lensing in LoTSS DR2: Extremely faint 144-MHz radio emission from two highly magnified quasars","volume":"505","author":"McKean","year":"2021","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1111\/j.1365-2966.2010.17028.x","article-title":"Quantifying cosmic variance","volume":"407","author":"Driver","year":"2010","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1051\/0004-6361:20040525","article-title":"A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17","volume":"421","author":"Wolf","year":"2004","journal-title":"Astron. Astrophys."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1088\/0004-637X\/690\/2\/1250","article-title":"Photometric Redshift and Classification for the XMM-COSMOS Sources","volume":"690","author":"Salvato","year":"2009","journal-title":"Astrophys. J."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"A20","DOI":"10.1051\/0004-6361\/201118111","article-title":"Quasi-stellar objects in the ALHAMBRA survey. I. Photometric redshift accuracy based on 23 optical-NIR filter photometry","volume":"542","author":"Matute","year":"2012","journal-title":"Astron. Astrophys."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"A2","DOI":"10.1051\/0004-6361\/201220873","article-title":"LOFAR: The LOw-Frequency ARray","volume":"556","author":"Wise","year":"2013","journal-title":"Astron. Astrophys."},{"key":"ref_81","first-page":"23","article-title":"The VizieR database of astronomical catalogues","volume":"143","author":"Ochsenbein","year":"2000","journal-title":"Astron. Astrophys."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Astropy Collaboration, Robitaille, T.P., Tollerud, E.J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A., and Price-Whelan, A.M. (2013). Astropy: A community Python package for astronomy. Astron. Astrophys., 558, A33.","DOI":"10.1051\/0004-6361\/201322068"},{"key":"ref_83","unstructured":"Astropy Collaboration, Price-Whelan, A.M., Sipocz, B.M., G\u00fcnther, H.M., Lim, P.L., Crawford, S.M., Conseil, S., Shupe, D.L., Craig, M.W., and Dencheva, N. (2018). The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package. Astron. J., 156, 123."},{"key":"ref_84","first-page":"29","article-title":"TOPCAT & STIL: Starlink Table\/VOTable Processing Software","volume":"Volume 347","author":"Shopbell","year":"2005","journal-title":"Proceedings of the Astronomical Data Analysis Software and Systems XIV"}],"container-title":["Galaxies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4434\/9\/4\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:31Z","timestamp":1760167351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4434\/9\/4\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":84,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["galaxies9040086"],"URL":"https:\/\/doi.org\/10.3390\/galaxies9040086","relation":{},"ISSN":["2075-4434"],"issn-type":[{"value":"2075-4434","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}