2024/05/10 by W. Turner, Paul Martini, Turner, Wynne +86
Engineering · #Advanced Measurement and Metrology Techniques #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences
paper · pdf · doi:10.48550/arxiv.2405.06743
openalex publication_date 2024/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
We present the Lyman-α Continuum Analysis Network (LyCAN), a Convolutional Neural Network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of 1040-1600 Angstroms based on the red side of the Lyman-α emission line (1216-1600 Angstroms). We developed synthetic spectra based on a Gaussian Mixture Model representation of Nonnegative Matrix Factorization (NMF) coefficients. These coefficients were derived from high-resolution, low-redshift (z<0.2) Hubble Space Telescope/Cosmic Origins Spectrograph quasar spectra. We supplemented this COS-based synthetic sample with an equal number of DESI Year 5 mock spectra. LyCAN performs extremely well on testing sets, achieving a median error in the forest region of 1.5% on the DESI mock sample, 2.0% on the COS-based synthetic sample, and 4.1% on the original COS spectra. LyCAN outperforms Principal Component Analysis (PCA)- and NMF-based prediction methods using the same training set by 40% or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of 2.1 ≤ z ≤ 4.2 and perform an absolute measurement of the evolution of the effective optical depth. This is the largest sample employed to measure the optical depth evolution to date. We fit a power-law of the form τ(z) = τ0 (1+z)γ to our measurements and find τ0 = (2.46 ± 0.14)×10-3 and γ= 3.62 ± 0.04. Our results show particular agreement with high-resolution, ground-based observations around z = 2, indicating that LyCAN is able to predict the quasar continuum in the forest region with only spectral information outside the forest.