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Multi-tracing the primordial Universe with future surveys
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abstract
The fluctuations generated by Inflation are nearly Gaussian in the simplest models, but may be non-Gaussian in more complex models, potentially leading to signatures in the late Universe. In particular, local type primordial non-Gaussianity induces scale-dependent bias in tracers of the matter distribution. This non-Gaussian imprint in the tracer power spectrum survives at late times on ultra-large scales where nonlinearity is negligible. In order to combat the problem of growing cosmic variance on these scales, we use a multi-tracer analysis that combines different tracers to maximise any primordial signal. Previous work has investigated the combination of a spectroscopic galaxy survey with a 21cm intensity mapping survey in single-dish mode. We extend this work by considering instead the case where the 21cm intensity mapping survey is optimised for interferometer mode. As examples, we use two multi-tracer pairs of surveys: one at high redshift ($1 \le z \le 2$) and one at very high redshift ($2 \le z \le 5$). The 21cm surveys are idealised surveys based on HIRAX and PUMA. We implement foreground-avoidance filters and use detailed models of the interferometer thermal noise. The galaxy surveys are idealised surveys based on Euclid and MegaMapper. Via a simple Fisher forecast, we illustrate the potential of the multi-tracer. Our results show a $\sim 20 - 30\%$ improvement in precision on local primordial non-Gaussianity from the multi-tracer. Furthermore, we investigate the effects on constraints of varying the parameter of non-Gaussian galaxy assembly bias and of varying the parameters of the intensity mapping foreground filters. We find that the non-Gaussian galaxy assembly bias parameter causes a greater change in the constraints on local primordial non-Gaussianity than the foreground filter parameters.
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Primordial non-Gaussianity -- the effects of relativistic and wide-angle corrections to the power spectrum
Including the quadrupole in power-spectrum forecasts improves f_NL precision by 45% to 63%, while neglected relativistic and wide-angle corrections shift f_NL by up to 0.6 sigma for MegaMapper.
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