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Published February 2026 | Version Published
Journal Article Open

The redshifts from 122 bands: Comparative redshift forecast for low-resolution spectra from SPHEREx and the 7-Dimensional Sky Survey (7DS)

Creators

  • 1. ROR icon Seoul National University
  • 2. ROR icon Korea Astronomy and Space Science Institute
  • 3. ROR icon Chungnam National University
  • 4. ROR icon Macquarie University
  • 5. ROR icon Kyungpook National University
  • 6. ROR icon Yonsei University
  • 7. ROR icon California Institute of Technology
  • 8. ROR icon Infrared Processing and Analysis Center
  • 9. ROR icon Pusan National University
  • 10. ROR icon Sejong University
  • 11. ROR icon Institut d'Astrophysique de Paris
  • 12. ROR icon Sorbonne University

Abstract

The recently initiated SPHEREx and 7DS surveys will deliver low-resolution spectra (R ∼ 20 − 130) for hundreds of millions of galaxies over the optical to near-infrared range (0.4 − 5.0 μm), covering a wide sky area without sample selection. These unique datasets will improve redshift estimation and provide a rich redshift catalog for the community. In this study, we forecast the performance of photometric redshift estimations using simulated SPHEREx and 7DS data. Four widely used template-fitting approaches and two machine-learning (ML) methods are used to derive photometric redshifts from low-resolution spectrophotometric data. We measured redshifts using mock catalogs based on the GAMA and COSMOS galaxy samples and achieved high precision for bright (13 < i < 18) galaxies, with σNMAD ≲ 0.005, bias ≲0.005, and a catastrophic failure rate ≲0.005 for all methods employed. We find that the combined SPHEREx + 7DS dataset significantly improves redshift estimation compared to using either the SPHEREx or 7DS datasets alone, highlighting the synergy between the two surveys. Moreover, we compare the redshift estimation performance across magnitude ranges for the different methods and examine the probability distribution functions (PDFs) produced by the template-fitting approaches. As a result, we identify some factors that can affect the redshift measurements, for example, treatments on dust extinction or inclusion of flux uncertainty in the ML model. We also show that the PDFs are relatively well calibrated, although the confidence intervals are generally underestimated, particularly for bright galaxies in the template-fitting methods. This study demonstrates the strong potential of SPHEREx and 7DS to deliver improved redshift measurements from low-resolution spectrophotometric data, underscoring the scientific value of jointly utilizing both datasets.

Copyright and License

© The Authors 2026. 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 are grateful to the anonymous referee for giving valuable comments to improve the content of this paper. This work was supported by the National Research Foundation of Korea (NRF) grant, No. 2021M3F7A1084525, funded by the Korean government (MSIT). HSH acknowledges the support of Samsung Electronics Co., Ltd. (Project Number IO220811-01945-01), the NRF grant funded by the Korean government (MSIT), NRF-2021R1A2C1094577, and Hyunsong Educational & Cultural Foundation. Y. K. was supported by the NRF grant funded by the Korean government (MSIT) (No. 2021R1C1C2091550). B. L. is supported by the NRF grant funded by the Korea government(MSIT) (No. NRF-2022R1C1C1008695). M. K. was supported by the NRF grant funded by the Korean government (MSIT) (No. RS-2024-00347548). SL acknowledges support from the NRF grant (RS-2025-00573214) funded by the Korean government(MSIT). S. K. was supported by the NRF grant funded by the Korean government (MSIT) (No. 2022R1C1C2005539). The work of HB was supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (RS-2025-25403440). D.K. acknowledges the support by the NRF grant (No. 2021R1C1C1013580) funded by the Korean government (MSIT). J.H.K. acknowledges the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant, No. RS-2021-II212068 funded by the Korean government (MSIT).

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Additional details

Related works

Is new version of
Discussion Paper: arXiv:2512.24537 (arXiv)

Funding

National Research Foundation of Korea
2021M3F7A1084525
Samsung Electronics
IO220811-01945-01
National Research Foundation of Korea
2021R1A2C1094577
Hyunsong Educational & Cultural Foundation
National Research Foundation of Korea
2021R1C1C2091550
National Research Foundation of Korea
2022R1C1C1008695
National Research Foundation of Korea
RS-2024-00347548
National Research Foundation of Korea
RS-2025-00573214
National Research Foundation of Korea
2022R1C1C2005539
National Research Foundation of Korea
RS-2025-25403440
National Research Foundation of Korea
2021R1C1C1013580
Institute of Information & Communications Technology Planning & Evaluation
RS-2021-II212068

Dates

Submitted
2025-08-27
Accepted
2025-12-21
Available
2026-02-23
Published online

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Caltech groups
Infrared Processing and Analysis Center (IPAC) , Division of Physics, Mathematics and Astronomy (PMA)
Publication Status
Published