Revisiting the [C  ii ] 158 μm line-intensity mapping power spectrum from the EoR using non-uniform line-luminosity scatter

• Published in: Monthly notices of the Royal Astronomical Society Vol. 518; no. 2; pp. 3074 - 3082
• Main Authors: Murmu, Chandra Shekhar, Olsen, Karen P, Greve, Thomas R, Majumdar, Suman, Datta, Kanan K, Scott, Bryan R, Leung, T K Daisy, Davé, Romeel, Popping, Gergö, Ochoa, Raul Ortega, Vizgan, David, Narayanan, Desika
• Format: Journal Article
• Language: English
• Published: 01.01.2023
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stac3304

Abstract Detecting the line-intensity mapping (LIM) signal from the galaxies of the epoch of reionization is an emerging tool to constrain their role in reionization. Ongoing and upcoming experiments target the signal fluctuations across the sky to reveal statistical and astrophysical properties of these galaxies via signal statistics, e.g. the power spectrum. Here, we revisit the [C ii]$_{158 \, \mu \text{m}}$ LIM power spectrum under non-uniform line–luminosity scatter, which has a halo-mass variation of statistical properties. Line–luminosity scatter from a cosmological hydrodynamic and radiative transfer simulation of galaxies at $z$ = 6 is considered in this study. We test the robustness of different model frameworks that interpret the impact of the line-luminosity scatter on the signal statistics. We use a simple power-law model to fit the scatter and demonstrate that the mean luminosity–halo mass correlation fit cannot preserve the mean intensity of the LIM signal (hence the clustering power spectrum) under non-uniform scatter. In our case, the mean intensity changes by ∼48 per cent compared to the mean correlation fit in contrast to the general case with semi-analytical scatter. However, we find that the prediction for the mean intensity from the most-probable fit can be modelled robustly, considering the generalized and more realistic non-uniform scatter. We also explore the possibility of diminishing luminosity bias under non-uniform scatter, affecting the clustering power spectrum, although this phenomenon might not be statistically significant. Therefore, we should adopt appropriate approaches that can consistently interpret the LIM power spectrum from observations.