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September 10, 2025Monthly Notices of the Royal Astronomical Society0 citationsOpen Access

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

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RJRui JunTTTom TheunsKMKana Moriwaki

Key Points

  • Secondary bias increases the two-halo term of the star formation rate-weighted power spectrum by 5% at z ∼ 1.5.
  • The correlation between star formation rate and halo bias enhances the one-halo term by 10% compared to random pairings.
  • Identifying halo concentration and satellite mass as secondary parameters can mitigate secondary bias effects.
  • These findings underscore the necessity of accounting for secondary bias when interpreting line-intensity mapping observations.

Abstract

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. Hα), 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 5percnt at z ∼ 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 percnt relative to random pairings. To mitigate secondary bias in the two-halo term, we identify halo concentration (for haloes with mass log Mh ≲ 12) and satellite mass (for log Mh ≳ 12) as effective secondary parameters. These results highlight the need to account for secondary bias when building mock catalogues and interpreting lim observations.

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Cite This Study

Jun et al. (2025) studied this question.

synapsesocial.com/papers/68c199e89b7b07f3a061b7bdhttps://doi.org/10.1093/mnras/staf1468
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