Speaker
Description
The 21 cm signal from neutral hydrogen is a key probe of the Epoch of Reionization (EoR), marking the universe’s transition from a cold, neutral state to a predominantly hot, ionized one, driven by the formation of the first stars and galaxies. Extracting this faint 21 cm signal from radio interferometric data requires precise gain calibration. However, traditional calibration methods are computationally expensive and time-intensive. More efficient calibration techniques are urgently needed with next-generation radio telescopes like the Square Kilometer Array (SKA) set to host hundreds of antennas.
To address this challenge, we present a sequential simulation-based inference (SBI) approach for direction-independent gain calibration, designed to automate and accelerate the process while improving scalability and accuracy. Once a forward model is established to generate simulations—transformations of the true sky image due to antenna gain variations—neural posterior estimation (NPE) with embedding networks is employed to infer the correct gain values for multiple antennas from the joint parameter-data distribution. We leverage GPU-accelerated parallelization to efficiently estimate the large number of gain parameters involved, within a feasible time frame.
The Bayesian framework enables robust uncertainty estimation, which traditional methods often overlook while facilitating faster and more reliable analysis of real SKA data. Future work could extend this approach to direction-dependent gains and other systematic effects or involve validating existing radio data. By integrating these techniques into the analysis pipeline, we can fully exploit SKA’s unprecedented sensitivity, significantly improving our ability to extract fundamental cosmological insights from large-scale observations.
| AI keywords | simulation-based inference; Neural Posterior Estimation; GPU Parallelization |
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