Leveraging cross-correlations and linear covariance-based filtering for line-intensity map reconstructions at linear scales
Dongwoo T Chung

TL;DR
This paper demonstrates that linear covariance-based filtering can effectively reconstruct large-scale high-redshift line-intensity fluctuations in simulations, even with bright interloper contamination, by leveraging cross-correlations with external data.
Contribution
It introduces the application of LCB filtering to line-intensity mapping, showing its potential for high-fidelity large-scale signal recovery in future surveys.
Findings
Achieves 70-90% cross-correlation on large scales in simulations.
Effective even with bright interloper emission.
Highlights importance of cross-correlations for high-redshift universe studies.
Abstract
We explore the possible application of linear covariance-based (LCB) filtering to line-intensity mapping (LIM) signal reconstructions. Originally introduced for reconstruction of the integrated Sachs-Wolfe effect in the cosmic microwave background, the LCB filter is an optimal map estimator that extends the simple Wiener filter by leveraging external correlated data. Given a detectable strong LIM-galaxy or LIM-LIM cross power spectrum, we show recovery of high-redshift, large-scale line-intensity fluctuations -- even in the presence of bright interloper emission -- in simulations of a futuristic [C II] LIM survey as well as simulated future iterations of the CO Mapping Array Project (COMAP). With sufficient galaxy abundances or low LIM survey noise, normalised cross-correlation between the LCB reconstruction and the true signal reaches 70-90% on large, linear comoving scales…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Radio Astronomy Observations and Technology
