Enhancing photometric redshift catalogs through color-space analysis: Application to KiDS-bright galaxies

Abstract

Copyright and License

© The Authors 2024.

Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acknowledgement

We thank our anonymous referee for their positive feedback and useful comments. PJ and MB are supported by the Polish National Science Center through grant no. 2020/38/E/ST9/00395. MB and WAH are supported by the Polish National Science Center through grants no. 2018/30/E/ST9/00698, 2018/31/G/ST9/03388 and 2020/39/B/ST9/03494. AHW is supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR), made possible by the Bundesministerium für Wirtschaft und Klimaschutz, and acknowledges funding from the German Science Foundation DFG, via the Collaborative Research Center SFB1491 “Cosmic Interacting Matters – From Source to Signal”. AD acknowledged support from ERC Consolidator Grant (No. 770935). This publication is part of the project “A rising tide: Galaxy intrinsic alignments as a new probe of cosmology and galaxy evolution” (with project number VI.Vidi.203.011) of the Talent programme Vidi which is (partly) financed by the Dutch Research Council (NWO). This work is also part of the Delta ITP consortium, a program of the Netherlands Organisation for Scientific Research (NWO) that is funded by the Dutch Ministry of Education, Culture and Science (OCW). CH acknowledges support from the European Research Council under grant number 647112, from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research, and the UK Science and Technology Facilities Council (STFC) under grant ST/V000594/1. HHi is supported by a DFG Heisenberg grant (Hi 1495/5-1), the DFG Collaborative Research Center SFB1491, as well as an ERC Consolidator Grant (No. 770935). KK acknowledges support from the Royal Society and Imperial College. CM acknowledges support under the grant number PID2021-128338NB-I00 from the Spanish Ministry of Science, and from the European Research Council under grant agreement No. 770935. SJN is supported by the US National Science Foundation (NSF) through grant AST-2108402. JLvdB is supported by an ERC Consolidator Grant (No. 770935). ZY acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research (Germany). MY acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 101053992). This work has made use of TOPCAT (Taylor 2005) software, as well as of PYTHON (www.python.org), including the packages NUMPY (van der Walt et al. 2011), SCIPY (Jones et al. 2001), ASTROPY (Astropy Collaboration 2022), PANDAS (McKinney 2010), SEABORN (Waskom 2021) and MATPLOTLIB (Hunter 2007). Author contributions. All authors contributed to the development and writing of this paper. The authorship list is given in two groups: the lead authors (PJ, MB, WAH, AHW), followed by an alphabetical group which includes those who have either made a significant contribution to the data products or to the scientific analysis.

Contributions

All authors contributed to the development and writing of this paper. The authorship list is given in two groups: the lead authors (PJ, MB, WAH, AHW), followed by an alphabetical group which includes those who have either made a significant contribution to the data products or to the scientific analysis.

Software References

This work has made use of TOPCAT (Taylor 2005) software, as well as of PYTHON (www.python.org), including the packages NUMPY (van der Walt et al. 2011), SCIPY (Jones et al. 2001), ASTROPY (Astropy Collaboration 2022), PANDAS (McKinney 2010), SEABORN (Waskom 2021) and MATPLOTLIB (Hunter 2007).