Accounting for Baryons in Cosmological Constraints from Cosmic Shear

TL;DR

This paper evaluates a method to mitigate baryonic effects in cosmic shear measurements, demonstrating it reduces biases in dark energy parameters for current surveys but may need improvement for future larger surveys.

Contribution

The study tests and validates a parameter-fitting method to account for baryonic effects in cosmic shear data, improving bias correction in dark energy parameter estimation.

Findings

Neglecting baryons causes biases larger than statistical errors in dark energy parameters.

The proposed method reduces residual biases below statistical errors for current surveys like DES.

For future large surveys, the method helps but additional strategies are needed to fully mitigate biases.

Abstract

One of the most pernicious theoretical systematics facing upcoming gravitational lensing surveys is the uncertainty introduced by the effects of baryons on the power spectrum of the convergence field. One method that has been proposed to account for these effects is to allow several additional parameters (that characterize dark matter halos) to vary and to fit lensing data to these halo parameters concurrently with the standard set of cosmological parameters. We test this method. In particular, we use this technique to model convergence power spectrum predictions from a set of cosmological simulations. We estimate biases in dark energy equation of state parameters that would be incurred if one were to fit the spectra predicted by the simulations either with no model for baryons, or with the proposed method. We show that neglecting baryonic effect leads to biases in dark energy parameters that are several times the statistical errors for a survey like the Dark Energy Survey. The proposed method to correct for baryonic effects renders the residual biases in dark energy equation of state parameters smaller than the statistical errors. These results suggest that this mitigation method may be applied to analyze convergence spectra from a survey like the Dark Energy Survey. For significantly larger surveys, such as will be carried out by the Large Synoptic Survey Telescope, the biases introduced by baryonic effects are much more significant. We show that this mitigation technique significantly reduces the biases for such larger surveys, but that a more effective mitigation strategy will need to be developed in order ensure that the residual biases in these surveys fall below the statistical errors.