Modeling Post-Reionization HI Distributions in Fuzzy Dark Matter Cosmologies Using Conditional Normalizing Flows

Kavli Affiliate: George Efstathiou

| First 5 Authors: Tibor Dome, Rumail Azhar, Anastasia Fialkov, ,

| Summary:

Upcoming 21 cm intensity mapping experiments like the Square Kilometer Array
(SKA) hold significant potential to constrain the properties of dark matter. In
this work, we model neutral hydrogen (HI) distributions using high-resolution
hydrodynamical $N$-body simulations of both cold dark matter (CDM) and fuzzy
dark matter (FDM) cosmologies in the post-reionization redshift range of
$z=3.42-4.94$. We show that the HI abundance and the HI column density
distribution function decrease in FDM-like cosmologies. Extreme FDM models with
$msim 10^{-22}$ eV are at odds with a range of measurements. Due to the
increased halo bias, the HI bias increases, paralleled by the damped Lya
(DLA) bias which we infer from the cross-section of DLAs. The DLA cross-section
distribution in extreme FDM models has a high median at the low-mass end, which
can be traced to the high column density of cosmic filaments. We study the
prospects of detecting the brightest HI peaks with SKA1-Low at $z=4.94$,
indicating moderate signal-to-noise ratios (SNR) at angular resolution
$theta_A = 2^{prime}$ with a rapidly declining SNR for lower values of
$theta_{A}$. After training the conditional normalizing flow network HIGlow on
2D HI maps, we interpolate its latent space of axion masses to predict the peak
flux for a new, synthetic FDM cosmology, finding good agreement with
expectations. This work thus underscores the potential of normalizing flows in
capturing complex, non-linear structures within HI maps, offering a versatile
tool for conditional sample generation and prediction tasks.

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