Search

We introduce a novel unbiased, cross-correlation estimator for the one-point statistics of cosmological random fields. One-point statistics are a useful tool for analysis of highly non-Gaussian density fields, while cross-correlations provide a powerful method for combining information from pairs of fields and separating them from noise and systematics. We derive a new Deconvolved Distribution Estimator that combines the useful properties of these two methods into one statistic. Using two example models of a toy Gaussian random field and a line intensity mapping survey, we demonstrate these properties quantitatively and show that the deconvolved distribution estimator can be used for inference. This new estimator can be applied to any pair of overlapping, non-Gaussian cosmological observations, including large-scale structure, the Sunyaev–Zeldovich effect, weak lensing, and many others. [ABSTRACT FROM AUTHOR]

We present the deconvolved distribution estimator (DDE), an extension of the voxel intensity distribution (VID), in the context of future observations proposed as part of the CO Mapping Array Project (COMAP). The DDE exploits the fact that the observed VID is a convolution of correlated signal intensity distributions and uncorrelated noise or interloper intensity distributions. By deconvolving the individual VID of two observables away from their joint VID in a Fourier-space operation, the DDE suppresses sensitivity to interloper emission while maintaining sensitivity to correlated components. The DDE thus improves upon the VID by reducing the relative influence of uncorrelated noise and interloper biases, which is useful in the context of COMAP observations that observe different rotational transitions of CO from the same comoving volume in different observing frequency bands. Fisher forecasts suggest that the theoretical sensitivity in the DDE allows significant improvements in constraining power compared to either the cross power spectrum or the individual VID data, and matches the constraining power of the combination of all other one- and two-point summary statistics. Future work should further investigate the covariance and model-dependent behaviour of this novel one-point cross-correlation statistic. [ABSTRACT FROM AUTHOR]