The power spectrum of galaxies from large to small scales: a line-intensity mapping perspective

TL;DR

This paper develops a model for the galaxy power spectrum weighted by star formation rate, accurately matching simulations and aiding interpretation of line-intensity mapping data across scales.

Contribution

The paper introduces a physically motivated model for the galaxy power spectrum in line-intensity mapping, including satellite galaxy effects and shot noise, validated against simulations.

Findings

Model reproduces simulation power spectrum within a few percent.

Satellite galaxies significantly impact bias and intensity estimates.

Fitting function aids interpretation of upcoming LIM survey data.

Abstract

We present a model for the power spectrum of the density field of galaxies weighted by their star formation rate. This weighting is relevant in line-intensity mapping (LIM) when the observed line luminosity is strongly correlated with star formation, as is the case for the H$\alpha$ line. Our model reproduces the measured power spectrum in the IllustrisTNG simulation to within a few per cent across all scales, with fitting parameters that have clear physical interpretations. On scales of tens of megaparsecs, the model accounts for the weighted non-linear bias of galaxies as well as halo exclusion (2-halo term). On smaller scales, it incorporates the weighted distribution of satellite galaxies within haloes (1-halo term). The random sampling of satellite galaxies introduces a galaxy shot noise term to the power spectrum on small scales, and their confinement to haloes introduces a halo shot noise term on large scales. Omitting satellite galaxies from the analysis results in an underestimation of both the large-scale bias and the mean intensity by approximately 30 per cent each at redshift 1.5. Assigning the intensity of satellites to the centre of their respective haloes affects the power spectrum on scales $k > 0.3$ h Mpc$^{-1}$. Our fitting function provides a well-motivated parametrisation that can be used to interpret data from upcoming LIM surveys.