Knowledge Bank

We present the Lyman-alpha Continuum Analysis Network (LyCAN), a Convolutional Neural Network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of $1040-1600\backslash angstrom$ based on only the red side of the Lyman-$\alpha$ emission line ($1216-1600\backslash angstrom$). 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 () 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 a factor of two or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of $2.0\le z\le 4.2$ 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 $\tau (z)=\tau 0(1+z)\gamma$ to our measurements and find $\tau 0=(2.46\pm 0.14)\times 10-3$ and $\gamma =3.62\pm 0.04$. 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.