Path Integral Marginalization for Cosmology: Scale Dependent Galaxy Bias & Intrinsic Alignments

Understand

We present a path-integral likelihood formalism that extends parameterized likelihood analyses to include continuous functions.

• The method finds the maximum likelihood point in function-space, and marginalizes over all possible functions, under the assumption of a Gaussian-distributed function-space.
• We apply our method to the problem of removing unknown systematic functions in two topical problems for dark energy research : scale-dependent galaxy bias in redshift surveys; and galaxy intrinsic alignments in cosmic shear surveys.
• We find that scale-dependent galaxy bias will degrade information on cosmological parameters unless the fractional variance in the bias function is known to 10%.