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 "title": "Spectroscopic redshift",
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 "excerpt": "A spectroscopic redshift is the displacement of a star's, galaxy's, or quasar's spectral lines from their rest wavelengths, expressed as z and about ten times more precise than photometric redshifts.",
 "snippet": "A spectroscopic redshift is the displacement of a star's, galaxy's, or quasar's spectral lines from their rest wavelengths, expressed as z and about ten times more precise than photometric redshifts.",
 "node": "physical.astronomy.cosmos.cosmos.techniques",
 "markdown": "# Spectroscopic redshift\n\nA spectroscopic redshift is the displacement of the spectral lines of a star, galaxy, or quasar from their rest wavelengths, expressed as the dimensionless quantity z.<sup>[1](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)</sup> Because the measurement comes from resolved spectral features rather than broad-band colors, it is roughly an order of magnitude more precise than a photometric redshift and serves as the calibration benchmark for photometric methods in modern cosmology surveys.\n\n| Key fact | Value |\n|---|---|\n| Definition | \\( 1 + z = \\lambda_{\\mathrm{obs}} / \\lambda_{\\mathrm{rest}} \\) <sup>[1](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)</sup> |\n| Recession velocity (small z) | \\( v = cz \\) <sup>[1](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)</sup> |\n| Typical precision | Better than \\( 10^{-3} \\) in z for resolving power \\( R > 200 \\) <sup>[2](https://ar5iv.labs.arxiv.org/html/1805.12574)</sup> |\n| 2dF Galaxy Redshift Survey quality | 98.4% reliable redshifts, rms uncertainty 85 km/s <sup>[3](https://authors.library.caltech.edu/records/53erm-fmv50)</sup> |\n| DESI Data Release 1 | 18.7M high-confidence redshifts (13.1M galaxies, 1.6M quasars, 4M stars) <sup>[4](https://iopscience.iop.org/article/10.3847/1538-3881/ae4c43/meta)</sup> |\n| JWST JADES DR4 | 3297 robust redshifts from 5190 targets, spanning z = 0.5 to 14.2 <sup>[5](https://doi.org/10.48550/arxiv.2510.01034)</sup> |\n\n## How it works\n\nRedshift is calculated from a measured and a known emitted wavelength: \\( z = (\\lambda_{\\mathrm{obs}} - \\lambda_{\\mathrm{emit}}) / \\lambda_{\\mathrm{emit}} \\), equivalently \\( 1 + z = \\lambda_{\\mathrm{obs}} / \\lambda_{\\mathrm{rest}} \\).<sup>[1](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)</sup><sup> • </sup><sup>[6](https://www.cosmos.esa.int/documents/401090/430753/VOSpec_Redshift_Tutorial.pdf)</sup> For nearby objects the shift is interpreted kinematically through the Doppler formula \\( v = c \\cdot \\Delta\\lambda / \\lambda \\), which reduces to \\( v = cz \\) at small redshift; a negative z is a blueshift, as for M33 at z = −0.000607, moving toward us at about 180 km/s.<sup>[1](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)</sup><sup> • </sup><sup>[6](https://www.cosmos.esa.int/documents/401090/430753/VOSpec_Redshift_Tutorial.pdf)</sup> For distant galaxies the dominant interpretation is cosmological: \\( 1 + z = d(0)/d(z) \\), the ratio of the present separation between two galaxies to their separation when the light was emitted, so z measures the scale of the universe at the time the light left the source.<sup>[1](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)</sup>\n\n**Which features carry the redshift.** Emission lines are the workhorses: [O II] λ3727 for galaxies at z < 1.5, Hα and other nebular lines (Hβ, [N II], [O III], [S II]) in the near-infrared at 1.5 < z < 2.5, and Lyα (rest wavelength 1215.67 Å) for quasars and high-redshift galaxies.<sup>[6](https://www.cosmos.esa.int/documents/401090/430753/VOSpec_Redshift_Tutorial.pdf)</sup><sup> • </sup><sup>[7](https://www.aanda.org/articles/aa/full_html/2017/12/aa31195-17/aa31195-17.html)</sup><sup> • </sup><sup>[8](https://openresearch-repository.anu.edu.au/bitstreams/fe602dff-3054-419d-9c84-d42386f88292/download)</sup> Absorption features and stellar continua matter for galaxies without strong emission: SDSS cross-correlation templates include roughly one high-S/N template per stellar type from B to L, plus emission-line galaxy, composite luminous red galaxy, and composite quasar templates co-added from 2000 spectra each.<sup>[9](https://sdss2.org/dr7/algorithms/redshift_type.php)</sup>\n\n## How it is done\n\nA practical redshift pipeline runs in a consistent sequence. The spectrum is first continuum-subtracted and wavelength-calibrated; SDSS then runs two independent analyses, an emission-line search using à trous wavelet peak detection and an absorption cross-correlation, and adopts the result with the higher confidence level.<sup>[9](https://sdss2.org/dr7/algorithms/redshift_type.php)</sup> The cross-correlation follows the technique: the continuum-subtracted spectrum is Fourier-transformed and convolved with each template transform, the three highest cross-correlation function peaks are fitted with parabolas, and the redshift error is taken from the widths of those peaks.<sup>[9](https://sdss2.org/dr7/algorithms/redshift_type.php)</sup>\n\n**Template fitting on a redshift grid.** Modern pipelines such as SDSS's idlspec2d and DESI's Redrock forward-model each spectrum at every trial redshift as a linear combination of PCA eigenspectra templates, minimizing \\( \\chi^{2} \\) given the measurement uncertainties.<sup>[10](http://sdss4.org/dr17/algorithms/redshifts)</sup><sup> • </sup><sup>[11](https://www.sdss3.org/dr9/algorithms/redshifts.php)</sup><sup> • </sup><sup>[12](http://www.osti.gov/pages/servlets/purl/2403619)</sup> SDSS explores galaxy trial redshifts from z = −0.01 to 1.00 in steps of 138 km/s (two pixels) and quasars from z = 0.0333 to 7.00 in 276 km/s steps; the five lowest-\\( \\chi^{2} \\) trial redshifts are then redetermined locally to sub-pixel accuracy, with errors from the curvature of the \\( \\chi^{2} \\) curve at the minimum.<sup>[10](http://sdss4.org/dr17/algorithms/redshifts)</sup>\n\n## Origin\n\nGalaxy spectra were photographically detected with sufficient signal-to-noise to measure their Doppler shifts reliably. In September 1912 he used the Brashear spectrograph on the Lowell 24-inch refractor for a 6-hour exposure of M31, and by 28 December 1912 concluded from a three-night exposure that M31 moves toward the Sun at about 300 km/s, an order of magnitude beyond any previously measured stellar or nebular radial velocity.<sup>[13](https://arxiv.org/pdf/1108.4864)</sup><sup> • </sup><sup>[14](https://cfraser.artsci.utoronto.ca/Slipher.pdf)</sup>\n\nThe digital era brought automated measurement: the RVSAO cross-correlation and emission-line software for redshifts and radial velocities was documented by Michael J. Kurtz and Douglas J. Mink in 1998 in the Publications of the Astronomical Society of the Pacific <sup>[15](https://doi.org/10.1086/316207)</sup>, and the [Sloan Digital Sky Survey](https://www.edgepedia.org/sloan-digital-sky-survey)'s technical summary appeared under [Donald G. York](https://www.edgepedia.org/donald-g-york) and colleagues in 2000 in The Astronomical Journal.<sup>[16](https://doi.org/10.1086/301513)</sup> The redmonster archetype-based classification software for eBOSS galaxies, with Timothy A. Hutchinson as first author and colleagues, followed in 2016 in The Astronomical Journal.<sup>[17](https://doi.org/10.3847/0004-6256/152/6/205)</sup>\n\n## Variants\n\n**Multi-fiber spectroscopy** places one fiber per target. The 2dF Galaxy Redshift Survey used the 2dF spectrograph on the Anglo-Australian Telescope to observe 400 objects simultaneously over a 2-degree field, measuring about 250,000 galaxies brighter than \\( b_{\\mathrm{J}} \\) = 19.45.<sup>[3](https://authors.library.caltech.edu/records/53erm-fmv50)</sup> DESI extends this approach with five redshift-scaffolded target classes over 14,000 deg².<sup>[4](https://iopscience.iop.org/article/10.3847/1538-3881/ae4c43/meta)</sup>\n\n**Integral-field spectroscopy** needs no target preselection. MUSE on the VLT covers 4650–9300 Å at R ~ 3000 and obtained 1338 high-quality redshifts in the Hubble Ultra Deep Field, an eightfold increase over previously known redshifts there, including 132 secure redshifts for sources with no HST counterparts found by blind emission-line searches in the data cube.<sup>[7](https://www.aanda.org/articles/aa/full_html/2017/12/aa31195-17/aa31195-17.html)</sup>\n\n**Near-infrared and space-based instruments** reach rest-frame optical lines at high redshift. JWST/NIRSpec observes with a low-dispersion prism (R = 30–300) and three medium-resolution gratings (R = 500–1500); JADES DR4 delivered 3297 robust redshifts out to z = 14.2, including 974 at z > 4.\n\n**Machine-learning fitting** is the newest variant. SpecPT, a transformer pre-trained on DESI data, predicts redshifts with normalized [median absolute deviation](https://www.edgepedia.org/median-absolute-deviation) of 0.0006 for BGS spectra and can process hundreds of thousands of spectra in minutes.<sup>[18](https://iopscience.iop.org/article/10.3847/1538-4357/ade053)</sup>\n\n## Applications\n\nAchieved precision depends on resolution, signal-to-noise, and spectral type. 2dFGRS redshifts with quality Q ≥ 3 are 98.4% reliable with an rms uncertainty of 85 km/s and 91.8% completeness over 2000 deg² to median depth z = 0.11.<sup>[3](https://authors.library.caltech.edu/records/53erm-fmv50)</sup>\n\n**Calibrating photometric redshifts.** Photometric redshifts, fitted from spectral energy distributions in broad or medium bands, are over an order of magnitude less precise than spectroscopic measurements <sup>[18](https://iopscience.iop.org/article/10.3847/1538-4357/ade053)</sup>, and only a few percent of sources in deep imaging surveys yield meaningful spectra even on 8-m telescopes.<sup>[2](https://ar5iv.labs.arxiv.org/html/1805.12574)</sup> Spectroscopic samples therefore anchor them: SDSS photo-z training sets use more than 830,000 main-sample galaxies (RMS 0.029) and over 1,060,000 BOSS galaxies (RMS 0.050).<sup>[19](https://www.sdss4.org/dr17/algorithms/photo-z/)</sup> Because incorrect spectroscopic redshifts in a training set degrade photo-z accuracy more severely than incompleteness does, this benchmark role makes spec-z quality flags consequential for downstream photometric pipelines.<sup>[20](https://par.nsf.gov/servlets/purl/10382166)</sup>\n\n**High redshift.** At z > 6 the optical lines move into the infrared and Lyα is often absorbed, so redshifts come from rest-frame ultraviolet lines or the prism continuum. JWST/NIRSpec prism and G395M spectroscopy of the dust-rich galaxy EGS-z11-R0 gives z_spec = 11.452 ± 0.021 from a weighted average of C IV λλ1548,1551 and C III] λ1908 line centroids, with detection S/N of only 3.9 and 3.2.<sup>[21](https://arxiv.org/abs/2603.15841)</sup>\n\n## Limitations and alternatives\n\n**Catastrophic redshifts and line misidentification.** The most common SDSS ZWARNING flag marks a change in reduced \\( \\chi^{2} \\) between the best and next-best fit of less than 0.01 absolute or less than 1% of the best model's value, signaling an unreliable redshift.<sup>[10](http://sdss4.org/dr17/algorithms/redshifts)</sup> A systematic failure mode is misidentifying bright emission lines: Hα λ6564 mistaken for the [O II] λ3726, 3729 doublet produces artifact concentrations near Δz/(1 + z) = −0.76.<sup>[18](https://iopscience.iop.org/article/10.3847/1538-4357/ade053)</sup>\n\n**Throughput limits.** Success rates for spectroscopic redshifts can fall below 50–70% for faint objects in deep surveys <sup>[2](https://ar5iv.labs.arxiv.org/html/1805.12574)</sup>, and spec-z is expensive in telescope time and hardest to obtain for high-redshift, low-luminosity sources.<sup>[20](https://par.nsf.gov/servlets/purl/10382166)</sup> Photometric redshifts cover far more sources at lower precision, so the gap relative to spectroscopy depends strongly on the photometric dataset.<sup>[7](https://www.aanda.org/articles/aa/full_html/2017/12/aa31195-17/aa31195-17.html)</sup>\n\n## References\n\n1. [The Hubble Diagram - Redshifts (SDSS SkyServer)](https://skyserver.sdss.org/dr1/en/proj/advanced/hubble/redshifts.asp)\n2. [The hundred flavours of photometric redshifts (review)](https://ar5iv.labs.arxiv.org/html/1805.12574)\n3. [The 2dF Galaxy Redshift Survey: spectra and redshifts (Colless et al. 2001, MNRAS 328, 1039)](https://authors.library.caltech.edu/records/53erm-fmv50)\n4. [Data Release 1 of the Dark Energy Spectroscopic Instrument (AJ)](https://iopscience.iop.org/article/10.3847/1538-3881/ae4c43/meta)\n5. [JADES Data Release 4 – Paper II. Data reduction, analysis, and emission-line fluxes of the complete spectroscopic sample](https://doi.org/10.48550/arxiv.2510.01034)\n6. [ESA VOSpec Redshift Tutorial](https://www.cosmos.esa.int/documents/401090/430753/VOSpec_Redshift_Tutorial.pdf)\n7. [The MUSE Hubble Ultra Deep Field Survey - II. Spectroscopic redshifts (A&A 2017)](https://www.aanda.org/articles/aa/full_html/2017/12/aa31195-17/aa31195-17.html)\n8. [ZFIRE: a MOSFIRE spectroscopic redshift survey of star-forming galaxies at 1.5 < z < 2.5](https://openresearch-repository.anu.edu.au/bitstreams/fe602dff-3054-419d-9c84-d42386f88292/download)\n9. [Algorithms: Spectroscopic Redshift and Type Determination - SDSS DR7](https://sdss2.org/dr7/algorithms/redshift_type.php)\n10. [Redshifts, Classifications and Velocity Dispersions | SDSS DR17](http://sdss4.org/dr17/algorithms/redshifts)\n11. [Redshifts, Classifications and Velocity Dispersions - SDSS-III (BOSS, idlspec2d)](https://www.sdss3.org/dr9/algorithms/redshifts.php)\n12. [DC3R2: DESI complete calibration of the colour–redshift relation secondary target survey (MNRAS/OSTI)](http://www.osti.gov/pages/servlets/purl/2403619)\n13. [Slipher's galaxy redshifts and the discovery of the velocity-distance relation (Reich 2011)](https://arxiv.org/pdf/1108.4864)\n14. [Vesto Slipher, Nebular Spectroscopy, and the Birth of Modern Cosmology, 1912-22](https://cfraser.artsci.utoronto.ca/Slipher.pdf)\n15. [Michael J.  Kurtz, Douglas J.  Mink (1998). RVSAO 2.0: Digital Redshifts and Radial Velocities. Publications of the Astronomical Society of the Pacific.](https://doi.org/10.1086/316207)\n16. [Donald G. York and colleagues (2000). The Sloan Digital Sky Survey: Technical Summary. The Astronomical Journal.](https://doi.org/10.1086/301513)\n17. [Timothy A. Hutchinson and colleagues (2016). REDSHIFT MEASUREMENT AND SPECTRAL CLASSIFICATION FOR eBOSS GALAXIES WITH THE REDMONSTER SOFTWARE. The Astronomical Journal.](https://doi.org/10.3847/0004-6256/152/6/205)\n18. [SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement (ApJ 2025)](https://iopscience.iop.org/article/10.3847/1538-4357/ade053)\n19. [Photometric Redshifts | SDSS](https://www.sdss4.org/dr17/algorithms/photo-z/)\n20. [The Sensitivity of GPz Estimates of Photo-z Posterior PDFs to Realistically Complex Training Set Imperfections](https://par.nsf.gov/servlets/purl/10382166)\n21. [EGS-z11-R0: a red, dust-rich galaxy at Cosmic Dawn (preprint)](https://arxiv.org/abs/2603.15841)\n\n---\n*Topic: Encyclopedia › Physical world and mathematics › Astronomy › Cosmology and observation › Observational techniques: astrometry, photometry, spectroscopy*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
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 "speakable": "A spectroscopic redshift is the displacement of a star's, galaxy's, or quasar's spectral lines from their rest wavelengths, expressed as z and about ten times more precise than photometric redshifts."
}