New measurements of the Lyman-alpha forest continuum and effective optical depth with LyCAN and DESI Y1 data
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Date
2024-03
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Abstract
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 $1040-1600\,\angstrom$ based on only the red side of the Lyman-$\alpha$ emission line ($1216-1600\,\angstrom$). 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 ($z<0.2$) 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 a factor of two or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of $2.0 \leq z \leq 4.2$ 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 $\tau_0 = (2.46 \pm 0.14)\times10^{-3}$ 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.
Description
Mathematical and Physical Sciences: 2nd Place (The Ohio State University Edward F. Hayes
Advanced Research Forum)
Keywords
astronomy, astrophysics, cosmology, intergalactic medium, artificial intelligence, neural network