Extracting non-Gaussian information from the Large-Scale Structure
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If you have a question about this talk, please contact Nathan Johnson-McDaniel.
One of the main of challenges in modern cosmology is to fully exploit datasets by extracting non-Gaussian (small scale) information. In this talk, I will present results following two different approaches. I will first present a full forward model for the neutral hydrogen counts-in-cell statistic obtained from the known matter counts-in-cell statistic together with a new non-linear bias function relating neutral hydrogen and matter. Secondly, I will show preliminary forecasts for a projected galaxy bispectrum analysis that allows to control systematics arising from non-linear redshift-space distortions.
This talk is part of the DAMTP Friday GR Seminar series.
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