Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks

Understand

We present forecasts on the detectability of Ultra-light axion-like particles (ULAP) from future 21cm radio observations around the epoch of reionization (EoR).

• We show that the axion as the dominant dark matter component has a significant impact on the reionization history due to the suppression of small scale density perturbations in the early universe.
• This behavior depends strongly on the mass of the axion particle.
• Using numerical simulations of the brightness temperature field of neutral hydrogen over a large redshift range, we construct a suite of training data.