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

¹ Astronomy Program, Department of Physics and Astronomy, Seoul National University 1, Gwanak-ro Gwanak-gu Seoul 08826, Republic of Korea
² Korea Astronomy and Space Science Institute (KASI) 776, Daedeok-daero Yuseong-gu Daejeon, Republic of Korea
³ SNU Astronomy Research Center, Seoul National University Seoul 08826, Republic of Korea
⁴ Department of Earth, Environmental & Space Sciences, Chungnam National University 99 Daehak-ro Yuseong-gu Daejeon, Republic of Korea
⁵ Australian Astronomical Optics – Macquarie University 105 Delhi Road North Ryde NSW 2113, Australia
⁶ Department of Astronomy and Atmospheric Sciences, Kyungpook National University 80, Daehak-ro Buk-gu Daegu, Republic of Korea
⁷ Department of Astronomy, Yonsei University, 50 Yonsei-ro Seodaemun-gu Seoul 03722, Republic of Korea
⁸ California Institute of Technology 1200 E California Blvd Pasadena CA 91125, USA
⁹ IPAC, California Institute of Technology, 1200 E California Blvd, 91125 Pasadena, USA, California Institute of Technology 1200 E California Blvd Pasadena CA 91125, USA
¹⁰ Department of Earth Sciences, Pusan National University Busan 46241, Republic of Korea
¹¹ School of Liberal Studies, Sejong University, 209 Neungdong-ro Gwangjin-Gu Seoul 05006, Republic of Korea
¹² Department of Physics and Astronomy, Sejong University, 209 Neungdong-ro Gwangjin-Gu Seoul 05006, Republic of Korea
¹³ Institut d’Astrophysique de Paris, UMR 7095, CNRS, Sorbonne Université 98 bis boulevard Arago F-75014 Paris, France

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Received: 27 August 2025
Accepted: 21 December 2025

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 σ_NMAD ≲ 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.