http://arxiv.org/abs/2212.06754
Large imaging surveys, such as LSST, rely on photometric redshifts and tomographic binning for 3x2pt analyses that combine galaxy clustering and weak lensing. In this paper, we propose a method for optimizing the tomographic binning choice for the lens sample of galaxies. We divide the CosmoDC2 and Buzzard simulated galaxy catalogs into a training set and an application set, where the training set is non-representative in a realistic way, and then estimate photometric redshifts for the application sets. The galaxies are sorted into redshift bins covering equal intervals of redshift or comoving distance, or with an equal number of galaxies in each bin, and we consider a generalized extension of these approaches. We find that placing equal numbers of galaxies in each bin produces the highest signal to noise of the initial binning choices, but that the choice of bin edges can be further optimized. We then train a neural network classifier to identify galaxies that are either highly likely to have accurate photometric redshift estimates, or highly likely to be sorted into the correct redshift bin. The neural network classifier is used to remove poor redshift estimates from the sample, and the results are compared to the case when none of the sample is removed. We find that the choice of bin edges has a larger impact than the sample selection with the NNCs, but the NNCs are able to recover ~70% of the loss in signal to noise that occurs when a non-representative training sample is used.
I. Moskowitz, E. Gawiser, A. Bault, et. al.
Wed, 14 Dec 22
60/69
Comments: 18 pages, 15 figures, submitted to ApJ