Towards Accurate Modeling of Line-Intensity Mapping One-Point Statistics: Including Extended Profiles

TL;DR

This paper improves the modeling of line-intensity mapping one-point statistics by including extended emission and resolution effects, validated with simulations, to enhance accuracy in cosmological and astrophysical parameter inference.

Contribution

It introduces the first model accounting for extended emission and resolution effects in LIM PDFs and validates it with simulations, highlighting the importance for unbiased parameter estimation.

Findings

Extended emission broadens the LIM PDF peak.

Limited resolution reduces high-intensity tail.

Model validation shows good qualitative agreement.

Abstract

Line-intensity mapping (LIM) is quickly attracting attention as an alternative technique to probe large-scale structure and galaxy formation and evolution at high redshift. LIM one-point statistics are motivated because they provide access to the highly non-Gaussian information present in line-intensity maps and contribute to break degeneracies between cosmology and astrophysics. Now that promising surveys are underway, an accurate model for the LIM probability distribution function (PDF) is necessary to employ one-point statistics. We consider the impact of extended emission and limited experimental resolution in the LIM PDF for the first time. We find that these effects result in a lower and broader peak at low intensities and a lower tail towards high intensities. Focusing on the distribution of intensities in the observed map, we perform the first model validation of LIM one-point statistics with simulations and find good qualitative agreement. We also discuss the impact on the covariance, and demonstrate that if not accounted for, large biases in the astrophysical parameters can be expected in parameter inference. These effects are also relevant for any summary statistic estimated from the LIM PDF, and must be implemented to avoid biased results. The comparison with simulations shows, however, that there are still deviations, mostly related with the modeling of the clustering of emitters, which encourage further development of the modeling of LIM one-point statistics.