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Harvesting the Lyα forest with convolutional neural networks

Cheng, Ting-Yun; Cooke, Ryan J; Rudie, Gwen

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Dr Sunny Cheng
Post Doctoral Research Associate

Gwen Rudie


We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low H I column density Lyα absorption systems (log NHI/cm−2 < 17) in the Lyα forest, and predict their physical properties, such as their H I column density (log NHI/cm−2), redshift (zHI), and Doppler width (bHI). Our CNN models are trained using simulated spectra (S/N ≃ 10), and we test their performance on high quality spectra of quasars at redshift z ∼ 2.5 − 2.9 observed with the High Resolution Echelle Spectrometer on the Keck I telescope. We find that ∼78% of the systems identified by our algorithm are listed in the manual Voigt profile fitting catalogue. We demonstrate that the performance of our CNN is stable and consistent for all simulated and observed spectra with S/N ≳ 10. Our model can therefore be consistently used to analyse the enormous number of both low and high S/N data available with current and future facilities. Our CNN provides state-of-the-art predictions within the range 12.5 ≤ log NHI/cm−2 < 15.5 with a mean absolute error of Δ(log NHI/cm−2) = 0.13, Δ(zHI) = 2.7 × 10−5, and Δ(bHI) = 4.1 km s−1. The CNN prediction costs <3 minutes per model per spectrum with a size of 120 000 pixels using a laptop computer. We demonstrate that CNNs can significantly increase the efficiency of analysing Lyα forest spectra, and thereby greatly increase the statistics of Lyα absorbers.


Cheng, T., Cooke, R. J., & Rudie, G. (2022). Harvesting the Lyα forest with convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 517, 755-775.

Journal Article Type Article
Acceptance Date Sep 9, 2022
Online Publication Date Sep 17, 2022
Publication Date 2022
Deposit Date Sep 30, 2022
Publicly Available Date Oct 5, 2022
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Volume 517
Pages 755-775
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Copyright Statement
© The Author(s) 2022. Published by Oxford University Press on behalf of The Royal Astronomical Society.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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