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Table 1.

Optimum tomographic binning strategies evaluated for different science cases.

Signal Simulation type Optimum binning Optimum No. bins Properties
choice
Realistic noise Equipopulated Convergence by Relative improvement between binning
around six to seven choices is up to 15% – represents motivation
Full 3 × 2 pt to use the equipopulated bins
No-noise Equipopulated Convergence by Relative gain between binning choices is
around six to eight slightly less significant (∼5–10%)
compared to the noisy simulation

Realistic noise Equal comoving Convergence by Minimal difference between different
distance around seven or binning choices (percent level)
Cosmic shear eight
No-noise Equal redshift Convergence by Improvement in (w0, wa) constraints by
width around seven increasing from two to ten equipopulated
bins is ≳99% for no-noise case
compared to ∼10% for noisy case

Realistic noise Equipopulated Convergence by Minimal difference in behaviour
Angular clustering around six to seven between no-noise and noisy simulations
No-noise Equipopulated Convergence by Minimal difference in behaviour between
around six to seven no-noise and noisy simulations

Notes. This table summarises the behaviour of the cosmic shear component, angular clustering component, and full 3 × 2 pt signal for different tomographic binning choices. The summary conclusions have been evaluated from the constraints on (w0, wa) measured across multiple binning choices and redshift bins in tomographic analyses of our simulated catalogues for both a no-noise and noisy set-up (see Sects. 6.1 & 6.2).

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