The LSST-DESC 3x2pt Tomography Optimization Challenge
  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.
