Scatter in the star formation rate-halo mass relation: secondary bias and its impact on line-intensity mapping

TL;DR

This paper investigates how secondary bias, specifically the correlation between star formation rate and halo bias, affects the line-intensity mapping power spectrum using IllustrisTNG simulations, revealing a 5-10% impact on key terms.

Contribution

It demonstrates the significance of secondary bias in LIM power spectra and identifies halo concentration and satellite mass as effective secondary parameters to mitigate this bias.

Findings

Secondary bias increases the two-halo term by 5% at z~1.5.

SFR correlation enhances the one-halo term by 10%.

Halo concentration and satellite mass can mitigate secondary bias.

Abstract

We use the IllustrisTNG cosmological hydrodynamical simulations to study the impact of secondary bias -- specifically, the correlation between star formation rate (SFR) and halo bias at fixed halo mass -- on the line-intensity mapping (LIM) power spectrum. In LIM, the galaxy contributions are flux-weighted, and therefore depend on the luminosity of emission line. We show that the (ensemble-averaged) large-scale two-halo term of the power spectrum depends only on the mean luminosity-halo mass relation if the scatter is uncorrelated with halo bias. However, when luminosity correlates with halo bias at fixed mass, this assumption breaks down. For many emission lines (e.g. H$\alpha$), luminosity is strongly correlated with SFR, making the SFR-weighted power spectrum important to study. In IllustrisTNG, secondary bias increases the two-halo term of the SFR-weighted power spectrum by 5 per cent at $z \sim 1.5$ compared to a model with random scatter. We also find that SFRs of central and satellite galaxies are correlated, enhancing the one-halo term -- which depends on the distribution of SFR inside the halo -- by 10 per cent relative to random pairings. To mitigate secondary bias in the two-halo term, we identify halo concentration (for haloes with mass $\log M_h \lesssim 12$) and satellite mass (for $\log M_h \gtrsim 12$) as effective secondary parameters. These results highlight the need to account for secondary bias when building mock catalogues and interpreting LIM observations.