Estimating the redshift distribution of photometric galaxy samples – II. Applications and tests of a new method

By Marcos Lima, C. Cunha, H. Oyaizu, Joshua A. Frieman, Huan Lin, Erin Sheldon. 2008

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In Lima et al. (2008) we presented a new method for estimating the redshift distribution, N(z), of a photometric galaxy sample, using photometric observables and weighted sampling from a spectroscopic subsample of the data. In this paper, we extend this method and explore various applications of it, using both simulations of and real data from the SDSS. In addition to estimating the redshift distribution for an entire sample, the weighting method enables accurate estimates of the redshift probability distribution, p(z), for each galaxy in a photometric sample. Use of p(z) in cosmological analyses can substantially reduce biases associated with traditional photometric redshifts, in which a single redshift estimate is associated with each galaxy. The weighting procedure also naturally indicates which galaxies in the photometric sample are expected to have accurate redshift estimates, namely those that lie in regions of photometric-observable space that are well sampled by the spectroscopic subsample. In addition to providing a method that has some advantages over standard photo-z estimates, the weights method can also be used in conjunction with photo-z estimates, e.g., by providing improved estimation of N(z) via deconvolution of N(zphot) and improved estimates of photo-z scatter and bias.

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Citations in the corpus

KiDS-450: The tomographic weak lensing power spectrum and constraints on cosmological parameters (2017, background): “…shortcomings, the photometric redshift distributions and the spectroscopic redshift distributions are re-weighted in a multidimensional magnitudespace, so that the volume density of objects in this magnitude space matches between photometric and spectroscopic catalogues (Lima et al. 2008).”
Dark Energy Survey Year 1 Results: redshift distributions of the weak-lensing source galaxies (2017, methodology): “This goal is unattainable without major observational, image processing, and simulation efforts, but we can approximate it with a method related to the one of Lima et al. (2008) and estimate the remaining uncertainties.”
Lensing is Low: Cosmology, Galaxy Formation, or New Physics? (2016, methodology): “The second requirement, however, is more difficult to quantify (see Lima et al. 2008; Cunha et al. 2009, 2012, 2014).”
KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing (2016, methodology): “(i) DIR: a weighted direct calibration obtained by a magnitudespace reweighting (Lima et al. 2008) of spectroscopic redshift catalogues that overlap with KiDS.”
KiDS+VIKING-450: Cosmic shear tomography with optical and infrared data (2018, methodology): “Hence, we use a “constant volume” approach as suggested by Lima et al. (2008).”
The many flavours of photometric redshifts (2018, methodology): “As in the case of templatefitting methods, ML can also improve on the results by considering additional information as a prior, often in the form of re-weighting the training data [59].”
Photometric Redshifts for Hyper Suprime-Cam Subaru Strategic Program Data Release 1 (2017, methodology): “Weights: We compute the specific weights such that the magnitude and magnitude error distribution of the training sample after applying the weights matches to the one for the entire wide layer photometric target sample (Lima et al. 2008).”
Photometric redshift analysis in the Dark Energy Survey Science Verification data (2014, methodology): “A detailed description of the method can be found in Lima et al. (2008).”
Euclid preparation (2021, methodology): “…such that they match the colourmagnitude properties of the target galaxy sample, thereby leveraging the relationship between galaxy colours, magnitudes, and redshifts to reconstruct the redshift distribution of the target sample (e.g., Lima et al. 2008; Cunha et al. 2009; Abdalla et al. 2008).”
KiDS+GAMA : cosmology constraints from a joint analysis of cosmic shear, galaxy-galaxy lensing, and angular clustering (2017, methodology): “The most robust is the weighted direct calibration method (hereafter referred to as DIR), which is based on the work of Lima et al. (2008).”
KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear (2019, methodology): “In addition, KiDS targets deep pencil-beam spectroscopic surveys permitting the redshift distributions to be determined via the weighted direct estimation, or ‘DIR’, approach (Lima et al. 2008; Hildebrandt et al. 2017; H20), which is fully decou- Article number, page 1 of 8 ar X iv :1 90 6.”
Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data (2018, background): “Galaxies from the spectroscopic sample that belong to the same groups are reweighted to mimic the distribution of the weak lensing source galaxy sample (Lima et al. 2008).”