Constraining single-field inflation with MegaMapper

被引:19
作者
Cabass, Giovanni [1 ]
Ivanov, Mikhail M. [1 ]
Philcox, Oliver H. E. [2 ,3 ]
Simonovic, Marko [4 ]
Zaldarriaga, Matias [1 ]
机构
[1] Inst Adv Study, Sch Nat Sci, 1 Einstein Dr, Princeton, NJ 08540 USA
[2] Columbia Univ, Ctr Theoret Phys, Dept Phys, New York, NY 10027 USA
[3] Simons Fdn, New York, NY 10010 USA
[4] CERN, Theoret Phys Dept, 1 Esplanade Particules, CH-1211 Geneva 23, Switzerland
关键词
PRIMORDIAL NON-GAUSSIANITY; SCALE-DEPENDENT BIAS; BISPECTRUM;
D O I
10.1016/j.physletb.2023.137912
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
We forecast the constraints on single-field inflation from the bispectrum of future high-redshift surveys such as MegaMapper. Considering non-local primordial non-Gaussianity (NLPNG), we find that current methods will yield constraints of order sigma (fNLeq ) N 23, sigma (f orth NL ) N 12 in a joint power-spectrum and bispectrum analysis, varying both nuisance parameters and cosmology, including a conservative range of scales. Fixing cosmological parameters and quadratic bias parameter relations, the limits tighten significantly to sigma (fNLeq ) N 17, sigma (forth NL ) N 8. These compare favorably with the forecasted bounds from CMB-S4: sigma (fNLeq ) N 21, sigma (fNLorth) N 9, with a combined constraint of sigma (fNLeq ) N 14, sigma (f orth NL ) N 7; this weakens only slightly if one instead combines with data from the Simons Observatory. We additionally perform a range of Fisher analyses for the error, forecasting the dependence on nuisance parameter marginalization, scale cuts, and survey strategy. Lack of knowledge of bias and counterterm parameters is found to significantly limit the information content; this could be ameliorated by tight simulation-based priors on the nuisance parameters. The error-bars decrease significantly as the number of observed galaxies and survey depth is increased: as expected, deep dense surveys are the most constraining, though it will be difficult to reach sigma(fNL) N 1 with current methods. The NLPNG constraints will tighten further with improved theoretical models (incorporating higher-loop corrections and improved understanding of nuisance parameters), as well as the inclusion of additional higher-order statistics.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3.
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页数:6
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