LSST Probes of Dark Energy: New Energy vs New Gravity

Statistics – Computation

Scientific paper

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Scientific paper

Is the late time acceleration of the universe due to new physics in the form of stress-energy or a departure from General Relativity? LSST will measure the shape, magnitude, and color of 4x109 galaxies to high S/N over 18,000 square degrees. These data will be used to separately measure the gravitational growth of mass structure and distance vs redshift to unprecedented precision by combining multiple probes in a joint analysis. Of the five LSST probes of dark energy, weak gravitational lensing (WL) and baryon acoustic oscillation (BAO) probes are particularly effective in combination.
By measuring the 2-D BAO scale in ugrizy-band photometric redshift-selected samples, LSST will determine the angular diameter distance to a dozen redshifts with sub percent-level errors. Reconstruction of the WL shear power spectrum on linear and weakly non-linear scales, and of the cross-correlation of shear measured in different photometric redshift bins provides a constraint on the evolution of dark energy that is complementary to the purely geometric measures provided by supernovae and BAO. Cross-correlation of the WL shear and BAO signal within redshift shells minimizes the sensitivity to systematics. LSST will also detect shear peaks, providing independent constraints. Tomographic study of the shear of background galaxies as a function of redshift allows a geometric test of dark energy.
To extract the dark energy signal and distinguish between the two forms of new physics, LSST will rely on accurate stellar point-spread functions (PSF) and unbiased reconstruction of galaxy image shapes from hundreds of exposures. Although a weighted co-added deep image has high S/N, it is a form of lossy compression. Bayesian forward modeling algorithms can in principle use all the information. We explore systematic effects on shape measurements and present tests of an algorithm called Multi-Fit, which appears to avoid PSF-induced shear systematics in a computationally efficient way.

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