Carregant...
Miniatura

Tipus de document

Article

Versió

Versió publicada

Data de publicació

Llicència de publicació

cc-by (c) Chen, Xingang et al., 2016
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/148666

The Future of Primordial Features with Large-Scale Structure Surveys

Títol de la revista

Director/Tutor

ISSN de la revista

Títol del volum

Resum

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

Matèries (anglès)

Citació

Citació

CHEN, Xingang, DVORKIN, Cora, HUANG, Zhiqi, NAMJOO, Mohammad hossein, VERDE, Licia. The Future of Primordial Features with Large-Scale Structure Surveys. _Journal of Cosmology and Astroparticle Physics_. 2016. Vol. 2016, núm. 11. [consulta: 25 de febrer de 2026]. ISSN: 1475-7516. [Disponible a: https://hdl.handle.net/2445/148666]

Exportar metadades

JSON - METS

Compartir registre