Simulation based inference pipeline of the ionization history from the 2D 21 cm power spectrum

Abstract The 21 cm signal contains a wealth of information about the formation of the first stars and the reionization of the intergalactic medium during the Cosmic Dawn (CD) and Epoch of Reionization (EoR). The timing of these important milestones has only roughly been constrained through indirect measurements (e.g. cosmic microwave background (CMB) optical depth, and Lyman-α forest). Therefore, inferring the neutral fraction over cosmic time is a goal of upcoming 21 cm experiments, such as the Square Kilometer Array (SKA). We contrast two approaches to infer astrophysical parameters and ionization history from 21 cm 2D power spectra (2DPS). We develop an emulator of the 21 cm 2DPS, trained on 21cmFAST simulations, taking into account the expected instrumental noise from the SKA and sample variance. We then perform simulation based inference (SBI) using neural posterior estimation (NPE). We compare training on datasets of noisy 2DPS obtained from 21cmFAST simulations and the emulator, to infer astrophysical parameters of interest. Using an emulator of the ionization history, we then obtain posterior distributions of the ionization history over the redshift range z ∼ 5-12. We demonstrate that both methods are capable of accurately recovering the ionization history and astrophysical parameters. However, coverage tests indicate that using a larger number of emulated samples instead of simulated samples does not improve predictions. This work suggests that due to the stochastic nature of the 2DPS, a more complex architecture than a dense model, with built in stochasticity, is needed to better emulate the 2DPS by accurately capturing the sample variance.