Reconstructing redshift distributions with photometric galaxy clustering

The accurate determination of the true redshift distributions in tomographic bins iscritical for cosmological constraints from photometric surveys. The proposed redshiftself-calibration method, which utilizes the photometric galaxy clustering alone, is highlyconvenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples inexternal calibration. However, the imperfection of the theoretical approximation on broad bins aswell as the flaw of the algorithm in previous work [1] risk the accuracy andapplication of the method. In this paper, we propose the improved self-calibration algorithm thatincorporates novel update rules, which effectively accounts for heteroskedastic weights and noisydata with negative values. The improved algorithm greatly expands the application range ofself-calibration method and accurately reconstructs the redshift distributions for various mockdata. Using the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument(DESI) survey, we find that the reconstructed results are comparable to the state-of-the-artexternal calibration. This suggests the exciting prospect of using photometric galaxy clusteringto reconstruct redshift distributions in the cosmological analysis of survey data.

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25 pages, 10 figures, accepted for publication in JCAP

galaxy clustering
redshift surveys

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