Deep Forest: Neural Network reconstruction of the Lyman-alpha forest

Abstract

We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Lyman-alpha forest. We train a Neural Network using redshift z=3 outputs from cosmological hydrodynamic simulations and mock datasets constructed from them. We evaluate how well the trained network is able to reconstruct the optical depth for Lyman-alpha forest absorption from noisy and often saturated transmitted flux data. The Neural Network outperforms an alternative reconstruction method involving log inversion and spline interpolation by approximately a factor of 2 in the optical depth root mean square error. We find no significant dependence in the improvement on input data signal to noise, although the gain is greatest in high optical depth regions. The Lyman-alpha forest optical depth studied here serves as a simple, one dimensional, example but the use of Deep Learning and simulations to approach the inverse problem in cosmology could be extended to other physical quantities and higher dimensional data.

10 pages, 7 figures, submitted to MNRAS. Code and data used at https://github.com/lhuangCMU/deep-learning-intergalactic-medium Changes: 11 pages, 7 figures. Further described how we chose our architecture, why the NN has difficulty predicting high values of optical depth, added more references, added additional panels to figs 3-6, and corrected fig 1 and mean optical depth value

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Cited by in corpus (8)

• Extracting cosmological parameters from N-body simulations using machine learning techniques
• Mapping Lyman-alpha forest three-dimensional large scale structure in real and redshift space
• A Novel Estimator for the Equation of State of the IGM by Ly$\alpha$ Forest Tomography
• Ly$\alpha$NNA: A Deep Learning Field-level Inference Machine for the Lyman-$\alpha$ Forest
• Deep Forest: Neural Network reconstruction of intergalactic medium temperature
• Reconstructing Large-scale Temperature Profiles around $z\sim 6$ Quasars
• Neural network emulator to constrain the high-$z$ IGM thermal state from Lyman-$\alpha$ forest flux auto-correlation function
• Efficient neutral-IGM inference from noisy 21-cm forest spectra with latent-space U-Net encoding and XGBoost