The LSST-DESC 3x2pt Tomography Optimization Challenge

• Published in: arXiv.org 2021-10
• Main Authors: Zuntz, Joe, Lanusse, François, Malz, Alex I, Wright, Angus H, Slosar, Anže, Abolfathi, Bela, Alonso, David, Bault, Abby, Bom, Clécio R, Brescia, Massimo, Broussard, Adam, Jean-Eric Campagne, Cavuoti, Stefano, Cypriano, Eduardo S, Fraga, Bernardo M O, Gawiser, Eric, Gonzalez, Elizabeth J, Green, Dylan, Hatfield, Peter, Iyer, Kartheik, Kirkby, David, Andrina Nicola, Nourbakhsh, Erfan, Park, Andy, Teixeira, Gabriel, Heitmann, Katrin, Kovacs, Eve, Yao-Yuan, Mao, the LSST Dark Energy Science Collaboration
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Cosmology; Dark energy; Galaxies; Optimization; Outliers (statistics); Photometry; Tomography
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2108.13418

This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included.