The many flavours of photometric redshifts

Salvato, M.; Ilbert, O.; Hoyle, B.

doi:10.1038/s41550-018-0478-0

Cited by 212 publications

(188 citation statements)

References 130 publications

(152 reference statements)

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“…Including the WISE colours in the algorithm significantly narrows the ∆z distribution (σ∆z = 0.38), which is further improved with the addition of the GALEX colours (σ∆z = 0.31). Without the need for visual inspection nor the filtering out of outliers, the spread is comparable to other studies (using similar and differing methods) which include NIR photometry (Ball et al 2008;Bovy et al 2012;Brescia et al 2013;Yang et al 2017;Duncan et al 2018;Salvato et al 2019).…”

Section: Resultssupporting

confidence: 78%

QSO photometric redshifts from SDSS, WISE, and GALEX colours

Curran

2020

Monthly Notices of the Royal Astronomical Society: Letters

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Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts, ∆z, is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread in ∆z for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread in ∆z for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2%) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.

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Section: Resultssupporting

confidence: 78%

QSO photometric redshifts from SDSS, WISE, and GALEX colours

Curran

2020

Monthly Notices of the Royal Astronomical Society: Letters

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“…The feasible way is to estimate their photo-z by multi-band observations. The accuracy of photo-z depends on observations, such as the overall wavelength coverage, the central position and band-width of filters, photometric accuracy, etc., as well as the methodology for photo-z estimates (Hildebrandt et al 2010;Salvato et al 2019).…”

Section: Impact Of Photometric Redshift Accuracy On Tomographic Peakmentioning

confidence: 99%

“…The photo-z errors are often characterized by the bias, the scatter, and the fraction of outliers, estimated using a subsample of galaxies with known spec-z (Salvato et al 2019). It is noted that not only these information, but also their uncertainties, namely, errors on errors, are important in tomographic WL studies.…”

Section: Impact Of Photometric Redshift Accuracy On Tomographic Peakmentioning

confidence: 99%

“…In addition to the direct spec-z calibration, variety of other photo-z calibration methods have been discussed in literature (Salvato et al 2019). The detailed tomographic WL peak studies taking into account complicated photo-z distributions and careful examinations of the requirements for different redshift calibration methods are beyond the scope of the current paper, and will be addressed in our future investigations.…”

Section: High Peak Tomography With Photometric Redshift Errorsmentioning

confidence: 99%

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Cosmological Studies from Tomographic Weak Lensing Peak Abundances and Impacts of Photo-z Errors

Yuan

Pan

Liu³

et al. 2019

ApJ

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Weak lensing peak abundance analyses have been applied in different surveys and demonstrated to be a powerful statistics in extracting cosmological information complementary to cosmic shear two-point correlation studies. Future large surveys with high number densities of galaxies enable tomographic peak analyses. Focusing on high peaks, we investigate quantitatively how the tomographic redshift binning can enhance the cosmological gains. We also perform detailed studies about the degradation of cosmological information due to photometric redshift (photo-z) errors. We show that for surveys with the number density of galaxies ∼ 40 arcmin −2 , the median redshift ∼ 1, and the survey area of ∼ 15000 deg 2 , the 4-bin tomographic peak analyses can reduce the error contours of (Ω m , σ 8 ) by a factor of 5 comparing to 2-D peak analyses in the ideal case of photo-z error being absent. More redshift bins can hardly lead to significantly better constraints. The photo-z error model here is parametrized by z bias and σ ph and the fiducial values of z bias = 0.003 and σ ph = 0.02 is taken. We find that using tomographic peak analyses can constrain the photo-z errors simultaneously with cosmological parameters. For 4-bin analyses, we can obtain σ(z bias )/z bias ∼ 10% and σ(σ ph )/σ ph ∼ 5% without assuming priors on them. Accordingly, the cosmological constraints on Ω m and σ 8 degrade by a factor of ∼ 2.2 and ∼ 1.8, respectively, with respect to zero uncertainties on photo-z parameters. We find that the uncertainty of z bias plays more significant roles in degrading the cosmological constraints than that of σ ph .

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“…Photometric redshifts are a key component for the exploitation of large imaging surveys (see, e.g., Salvato et al 2019, for a review). They are a cheap alternative to spectroscopic surveys for the measurement of distances of millions of galaxies.…”

Section: Introductionmentioning

confidence: 99%

PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

Shuntov

Pasquet

Arnouts

et al. 2020

A&A

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Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. As recently shown, photometric redshifts can be efficiently combined with the cosmic web extracted from overlapping spectroscopic surveys to improve their accuracy. In this paper we apply a similar method using a new generation of photometric redshifts based on a convolution neural network (CNN). The CNN is trained on the SDSS images with the main galaxy sample (SDSS-MGS, r ≤ 17.8) and the GAMA spectroscopic redshifts up to r ∼ 19.8. The mapping of the cosmic web is obtained with 680,000 spectroscopic redshifts from the MGS and BOSS surveys. The redshift probability distribution functions (PDF), which are well calibrated (unbiased and narrow, ≤ 120 Mpc), intercept a few cosmic web structures along the line of sight. Combining these PDFs with the density field distribution provides new photometric redshifts, z web , whose accuracy is improved by a factor of two (i.e., σ ∼ 0.004(1 + z)) for galaxies with r ≤ 17.8. For half of them, the distance accuracy is better than 10 cMpc. The narrower the original PDF, the larger the boost in accuracy. No gain is observed for original PDFs wider than 0.03. The final z web PDFs also appear well calibrated. The method performs slightly better for passive galaxies than star-forming ones, and for galaxies in massive groups since these populations better trace the underlying large-scale structure. Reducing the spectroscopic sampling by a factor of 8 still improves the photometric redshift accuracy by 25%. Finally, extending the method to galaxies fainter than the MGS limit still improves the redshift estimates for 70% of the galaxies, with a gain in accuracy of 20% at low z where the resolution of the cosmic web is the highest. As two competing factors contribute to the performance of the method, the photometric redshift accuracy and the resolution of the cosmic web, the benefit of combining cosmological imaging surveys with spectroscopic surveys at higher redshift remains to be evaluated.

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The many flavours of photometric redshifts

Cited by 212 publications

References 130 publications

QSO photometric redshifts from SDSS, WISE, and GALEX colours

QSO photometric redshifts from SDSS, WISE, and GALEX colours

Cosmological Studies from Tomographic Weak Lensing Peak Abundances and Impacts of Photo-z Errors

PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

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