Testing Inflation with Large Scale Structure: Connecting Hopes with Reality

By M. Alvarez, T. Baldauf, J. Richard Bond, N. Dalal, R. D. Putter, O. Dor'e, D. Green, C. Hirata, Zhiqi Huang, D. Huterer and 18 more. 2014

148 citations recorded by the source corpus.

The statistics of primordial curvature fluctuations are our window into the period of inflation, where these fluctuations were generated. To date, the cosmic microwave background has been the dominant source of information about these perturbations. Large scale structure is however from where drastic improvements should originate. In this paper, we explain the theoretical motivations for pursuing such measurements and the challenges that lie ahead. In particular, we discuss and identify theoretical targets regarding the measurement of primordial non-Gaussianity. We argue that when quantified in terms of the local (equilateral) template amplitude $f_{\rm NL}^{\rm loc}$ ($f_{\rm NL}^{\rm eq}$), natural target levels of sensitivity are $\Delta f_{\rm NL}^{\rm loc, eq.} \simeq 1$. We highlight that such levels are within reach of future surveys by measuring 2-, 3- and 4-point statistics of the galaxy spatial distribution. This paper summarizes a workshop held at CITA (University of Toronto) on October 23-24, 2014.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/4d714a7b059202cc73b30b8a6d24faf83c725f87

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Citations in the corpus

CMB-S4 Science Case, Reference Design, and Project Plan (2019, background): “1 [104].”
Planck 2018 results. IX. Constraints on primordial non-Gaussianity (2019, background): “…shapes would allow us to distinguish standard slowroll from more complex single-field scenarios, such as those characterized by higher-derivative kinetic terms or slow-rollbreaking features in the inflaton potential (see e.g., Alvarez et al. 2014; Finelli et al. 2018, and references therein).”
Non-Gaussianity as a particle detector (2016, background): “Digging deeper will require new cosmological probes, such as observations of the largescale structure (LSS) of the universe [55] and the tomography of the 21cm transition of neutral hydrogen gas [56].”
A volumetric deep Convolutional Neural Network for simulation of dark matter halo catalogues (2018, background): “…† E-mail: gstein@cita.utoronto.ca credible promise for constraining fundamental physics such as gravity on large scales (Masui et al. 2010), the equationof-state of dark energy (Shaw et al. 2015), neutrino masses (Inman et al. 2016), or the physics of inflation (Alvarez et al. 2014).”
The Simons Observatory: science goals and forecasts (2018, background): “5.3.2, an interesting theoretical target widely shared within the community is fNL ∼ O(1), both for local and equilateral non-Gaussianities (Alvarez et al. 2014).”