Speaker
Description
The accurate determination of the true redshift distributions in tomographic bins is critical for cosmological constraints from photometric surveys. We developed a redshift self-calibration method, which utilizes the photometric galaxy clustering alone, is highly convenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples in external calibration. By refining the update rules in the iterative process, the algorithm successfully handled negative values and uncertainties in observational data, markedly improving the accuracy of redshift distributions. Using the luminous red galaxy (LRG) photometric sample of the Dark Energy Spectroscopic Instrument (DESI) survey, we find that the reconstructed results are comparable to the state-of-the-art external calibration. This suggests the exciting prospect of using photometric galaxy clustering to reconstruct redshift distributions in the cosmological analysis of survey data.
