Predicting Primordial Non-Gaussianity Measurements in the CSST Spectroscopic Survey
Abstract
Primordial non-Gaussianity (PNG) is a fundamental probe of the physics of the early Universe and inflation. Here we present a comprehensive study of the constraints on the local-type PNG parameter, fNL, for the spectroscopic galaxy survey of the upcoming Chinese Space-station Survey Telescope (CSST). Utilizing the high-resolution Jiutian N-body simulation suite, we construct realistic mock catalogs for emission line galaxies (ELGs) at three representative redshifts z=0.3, 0.6, and 0.9. The expected CSST observational characteristics are also considered, including redshift uncertainties and selection functions based on signal-to-noise ratios of emission lines. We develop a robust analysis framework for the redshift-space galaxy power spectrum and bispectrum that accounts for redshift-space distortions, scale-dependent bias, and nonlinear effects. For the parameter p in the PNG bias bϕ with p=0.55, we find fNL=−4±43 if only using the power spectrum data, and fNL=3±41 after combining the bispectrum data, reducing the uncertainty by ∼5%.For the p=1 case, the constraint results are fNL=−3±86 and 54+70−80 for comparison. This work demonstrates the potential of the Stage IV surveys like CSST to probe inflationary physics, and highlights the importance of higher-order statistics in extracting information from large-scale structure surveys.
Related
- Large primordial non-Gaussianity from transient turns in Higgs-R2 inflation
- Initial clustering of primordial black holes: A general formulation for arbitrary local non-Gaussianity
- Probing the local primordial non-Gaussianity with clustering of FRB dispersion measure and galaxies
- Coverage is not enough: Frequentist tests of simulation-based inference for primordial non-Gaussianity
- Primordial Perturbations, Chladni Resonances and Non-Gaussianity within the HRDCC Framework
BibTeX
@misc{duan2026predicting,
title = {{Predicting Primordial Non-Gaussianity Measurements in the CSST Spectroscopic Survey}},
author = {Jiangnan Duan and Yan Gong and Qi Xiong and Haitao Miao and Xuelei Chen and Qi Guo and Ming Li and Yun Liu and Wenxiang Pei},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.18744},
eprint = {2606.18744},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.18744},
}
RIS
TY - UNPB
TI - Predicting Primordial Non-Gaussianity Measurements in the CSST Spectroscopic Survey
AU - Jiangnan Duan
AU - Yan Gong
AU - Qi Xiong
AU - Haitao Miao
AU - Xuelei Chen
AU - Qi Guo
AU - Ming Li
AU - Yun Liu
AU - Wenxiang Pei
PY - 2026
DA - 2026/10/31
PB - arXiv
DO - 10.48550/arxiv.2606.18744
UR - https://arxiv.org/abs/2606.18744
AB - Primordial non-Gaussianity (PNG) is a fundamental probe of the physics of the early Universe and inflation. Here we present a comprehensive study of the constraints on the local-type PNG parameter, f_NL, for the spectroscopic galaxy survey of the upcoming Chinese Space-station Survey Telescope (CSST). Utilizing the high-resolution Jiutian N-body simulation suite, we construct realistic mock catalogs for emission line galaxies (ELGs) at three representative redshifts z=0.3, 0.6, and 0.9. The expected CSST observational characteristics are also considered, including redshift uncertainties and selection functions based on signal-to-noise ratios of emission lines. We develop a robust analysis framework for the redshift-space galaxy power spectrum and bispectrum that accounts for redshift-space distortions, scale-dependent bias, and nonlinear effects. For the parameter p in the PNG bias b_ϕ with p=0.55, we find f_NL=−4±43 if only using the power spectrum data, and f_NL=3±41 after combining the bispectrum data, reducing the uncertainty by ∼5%.For the p=1 case, the constraint results are f_NL=−3±86 and 54^+70_−80 for comparison. This work demonstrates the potential of the Stage IV surveys like CSST to probe inflationary physics, and highlights the importance of higher-order statistics in extracting information from large-scale structure surveys.
ER -