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arXiv · 2501.11872

FAST drift scan survey for HI intensity mapping: simulation on Bayesian-stacking-based HI mass function estimation

Jiaxin Wang·Yichao Li·Hengxing Pan·Furen Deng·Diyang Liu·Wenxiu Yang·Wenkai Hu·Yougang Wang·Xin Zhang·Xuelei Chen
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Abstract

This study investigates the estimation of the neutral hydrogen (HI) mass function (HIMF) using a Bayesian stacking approach with simulated data for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) HI intensity mapping (HIIM) drift-scan surveys. Using data from the IllustrisTNG simulation, we construct HI sky cubes at redshift $z\sim0.1$ and the corresponding optical galaxy catalogs, simulating FAST observations under various survey strategies, including pilot, deep-field, and ultradeep-field surveys. The HIMF is measured for distinct galaxy populations -- classified by optical properties into red, blue, and bluer galaxies -- and injected with systematic effects such as observational noise and flux confusion caused by the FAST beam. The results show that Bayesian stacking significantly enhances HIMF measurements. For red and blue galaxies, the HIMF can be well constrained with pilot surveys, while deeper surveys are required for the bluer galaxy population. Our analysis also reveals that sample variance dominates over observational noise, emphasizing the importance of wide-field surveys to improve constraints. Furthermore, flux confusion shifts the HIMF toward higher masses, which we address using a transfer function for correction. Finally, we explore the effects of intrinsic sample incompleteness and propose a framework to quantify its impact. This work lays the groundwork for future \hiMF studies with FAST HIIM, addressing key challenges and enabling robust analyses of HI content across galaxy populations.

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Jiaxin Wang, Yichao Li, Hengxing Pan, Furen Deng, Diyang Liu, Wenxiu Yang, Wenkai Hu, Yougang Wang, Xin Zhang, Xuelei Chen. 2025-01-21. FAST drift scan survey for HI intensity mapping: simulation on Bayesian-stacking-based HI mass function estimation. https://arxiv.org/abs/2501.11872

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