CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization

Abstract

The observation of the redshifted 21-cm signal from the intergalactic medium will probe the epoch of reionization (EoR) with unprecedented detail. Various simulations are being developed and used to predict and understand the nature and morphology of this signal. However, these simulations are computationally very expensive and time-consuming to produce in large numbers. To overcome this problem, an efficient field-level emulator of this signal is required. However, the EoR 21-cm signal is highly non-Gaussian; therefore, capturing the correlations between different scales of this signal, which is directly related to the evolution of the reionization, with the neural network is quite difficult. Here, we introduce CosmoUiT, a UNet integrated vision transformer-based architecture, to overcome these difficulties. CosmoUiT emulates the 3D cubes of 21-cm signal from the EoR, for a given input dark matter density field, halo density field, and reionization parameters. CosmoUiT uses the multi-head self-attention mechanism of the transformer to capture the long-range dependencies and convolutional layers in the UNet to capture the small-scale variations in the target 21-cm field. Furthermore, the training of the emulator is conditioned on the input reionization parameters such that it gives a fast and accurate prediction of the 21-cm field for different sets of input reionization parameters. We evaluate the predictions of our emulator by comparing various statistics (e.g., bubble size distribution, power spectrum) and morphological features of the emulated and simulated maps. We further demonstrate that this vision transformer-based architecture can emulate the entire 3D 21-cm signal cube with high accuracy at both large and small scales.