The DOI matches its Crossref record.
Slicing Through Multicolor Space: Galaxy Redshifts from Broadband Photometry
Open accessAbstract
As a means of better understanding the evolution of optically selected galaxies we consider the distribution of galaxies within the multicolor space U, B_J_, R_F_, and I_N_. We find that they form an almost planar distribution out to B_J_ = 22.5 and z < 0.3. The position of a galaxy within this plane is dependent on its redshift, luminosity, and spectral type. While in the original U, B_J_, R_F_, and I_N_ space these properties are highly correlated, we can define an optimal rotation of the photometric axes that makes much of this information orthogonal. Fitting the observed spectroscopic redshifts with a quadratic function of the four magnitudes we show that redshifts for galaxies can be estimated to an accuracy better than {DELTA}z = 0.05. This dispersion is due to the photometric uncertainties within the photographic data. Assuming no galaxy evolution we derive a set of simulated galaxy fluxes in the U, J, F, and N passbands. Using these data we investigate how the redshift is encoded within the broadband magnitudes and the intrinsic dispersion of the photometric-redshift relation. We find that the signal that defines a galaxy's photometric redshift is not related to specific absorption or emission lines but comes from the break in the overall shape of the galaxy continuum at around 4000 A. Using high signal-to-noise photometric data we estimate that it is possible to achieve an intrinsic dispersion of less than δz = 0.02.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Sloan Digital Sky Survey: Early Data Release
Stoughton, C., Lupton, R. H., Bernardi, M., Blanton, M. R., Burles, S., Castander, F. J., Connolly, A. J., Eisenstein, D. J., Frieman, J. A., Hennessy, G. S., et al. (2002) · The Astronomical Journal · doi:10.1086/324741 · Cited by 2,390
Self-citationData mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
Budavari, T., Connolly, A. J., Szalay, A. S., Szapudi, I., Csabai, I., Scranton, R., Bahcall, N. A., Brinkmann, J., Eisenstein, D. J., Frieman, J. A., et al. (2003) · The Astrophysical Journal · doi:10.1086/377168 · Cited by 135
Self-citationSelf-supervised Representation Learning for Astronomical Images
Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. (2021) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/abf2c7 · Cited by 79
“Photo-z models take multiband galaxy photometry and use template fitting (Loh & Spillar 1986), ML (Connolly et al. 1995), or hybrid methods to produce estimates for the galaxyʼs redshift z and its associated probability density function.”
MethodUsing neural networks to estimate redshift distributions. An application to CFHTLenS
Bonnett, C. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv230 · Cited by 76
Metadata
- Authors
- Connolly, A. J., Csabai, I., Szalay, A. S., Koo, D. C., Kron, R. G., & Munn, J. A.
- Venue
- The Astronomical Journal
- Publisher
- American Astronomical Society
- Published
- 1995-12
- Volume
- 110
- Pages
- 2655
- ISSN
- 0004-6256 (ISSN-L)
- OpenAlex
- W2069970095
- Cited by
- 332
- References
- 0
- FWCI
- 15.10
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Slicing Through Multicolor Space: Galaxy Redshifts from Broadband Photometry. https://app.cochat.ai/papers/10.1086/117720
- The paper
- Connolly, A. J., Csabai, I., Szalay, A. S., Koo, D. C., Kron, R. G., & Munn, J. A. (1995). Slicing Through Multicolor Space: Galaxy Redshifts from Broadband Photometry. The Astronomical Journal. https://doi.org/10.1086/117720
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.