Modelling post-reionization H i distributions in fuzzy dark matter cosmologies using conditional normalizing flows

Abstract

ABSTRACT 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 (H i) 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 H i abundance decreases in FDM-like cosmologies. Extreme FDM models with m ∼ 10−22 eV are at odds with a range of measurements. Due to the increased halo bias, the H i bias increases, paralleled by the damped Lyman-α (DLA) bias which we infer from the cross-section of DLAs. The distribution of the latter 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. FDM models exhibit a very similar abundance of DLAs compared to CDM while sub-DLAs are already less abundant. We study the prospects of detecting the brightest H i peaks with SKA1-Low at z = 4.94, indicating moderate signal-to-noise ratios (SNRs) at angular resolution θA = 2 arcmin with a rapidly declining SNR for lower values of θA. After training the conditional normalizing flow network HIGlow on 2D H i 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 H i maps, offering a versatile tool for conditional sample generation and prediction tasks.