Primordial Power Spectrum features and $f_{NL}$ constraints
Stefano Gariazzo, Laura Lopez-Honorez, Olga Mena

TL;DR
This paper investigates how relaxing the primordial power spectrum assumptions affects future constraints on local non-gaussianity parameter $f_{NL}$, highlighting potential biases and the importance of combining multiple data sources for robust results.
Contribution
It provides a model-independent analysis of the impact of primordial power spectrum features on $f_{NL}$ constraints from future large scale structure surveys.
Findings
Errors on $f_{NL}$ increase by 60% when spectrum features are ignored.
Fitting to an incorrect spectrum can bias $f_{NL}$ by about 2.5.
Adding CMB priors yields nearly unbiased $f_{NL}$ estimates with errors close to standard models.
Abstract
The simplest models of inflation predict small non-gaussianities and a featureless power spectrum. However, there exist a large number of well-motivated theoretical scenarios in which large non-gaussianties could be generated. In general, in these scenarios the primordial power spectrum will deviate from its standard power law shape. We study, in a model-independent manner, the constraints from future large scale structure surveys on the local non-gaussianity parameter when the standard power law assumption for the primordial power spectrum is relaxed. If the analyses are restricted to the large scale-dependent bias induced in the linear matter power spectrum by non-gaussianites, the errors on the parameter could be increased by when exploiting data from the future DESI survey, if dealing with only one possible dark matter tracer. In the same context, a…
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