21cmVAE: A Very Accurate Emulator of the 21-cm Global Signal

Abstract

Considerable observational efforts are being dedicated to measuring the sky-averaged (global) 21-cm signal of neutral hydrogen from Cosmic Dawn and the Epoch of Reionization. Deriving observational constraints on the astrophysics of this era requires modeling tools that can quickly and accurately generate theoretical signals across the wide astrophysical parameter space. For this purpose artificial neural networks were used to create the only two existing global signal emulators, 21cmGEM and globalemu. In this paper we introduce 21cmVAE, a neural network-based global signal emulator, trained on the same dataset of ~30,000 global signals as the other two emulators, but with a more direct prediction algorithm that prioritizes accuracy and simplicity. Using neural networks, we compute derivatives of the signals with respect to the astrophysical parameters and establish the most important astrophysical processes that drive the global 21-cm signal at different epochs. 21cmVAE has a relative rms error of only 0.34 - equivalently 0.54 mK - on average, which is a significant improvement compared to the existing emulators, and a run time of 0.04 seconds per parameter set. The emulator, the code, and the processed datasets are publicly available at https://github.com/christianhbye/21cmVAE and through https://zenodo.org/record/5904939.

11 pages, 7 figures, 2 tables (including appendices). In press (ApJ). Code available at https://github.com/christianhbye/21cmVAE

References in corpus (5)

• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• Results from EDGES High-Band: I. Constraints on Phenomenological Models for the Global $21$ cm Signal
• Results from EDGES High-Band: III. New Constraints on Parameters of the Early Universe
• GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation
• A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs

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• Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere
• A Multi-Fidelity Emulator for the Lyman-$\alpha$ Forest Flux Power Spectrum
• Simulation-Based Inference of the sky-averaged 21-cm signal from CD-EoR with REACH
• RHINO: A large horn antenna for detecting the 21cm global signal
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• Comparing sampling techniques to chart parameter space of 21 cm Global signal with Artificial Neural Networks
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• In Search of Global 21-cm Signal using Artificial Neural Network in light of ARCADE 2
• Exploring Machine Learning Regression Models for Advancing Foreground Mitigation and Global 21cm Signal Parameter Extraction
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• Simulation based inference of the ionization history from the 2D 21 cm power spectrum
• Accelerating reionization constraints: An ANN-emulator framework for the SCRIPT Semi-numerical Model
• On the accuracy of posterior recovery with neural network emulators
• A bubble size distribution model for the Epoch of Reionization
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