We present the Lyα Continuum Analysis Network (LyCAN), a convolutional neural network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of 1040–1600 Å based on the red side of the Lyα emission line (1216–1600 Å). 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 (COS) 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 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.

中文翻译：

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使用 LyCAN 和 DESI Y1 数据对 Lyα 森林连续体和有效光学深度进行新测量


我们提出了 Lyα 连续体分析网络 （LyCAN），这是一种卷积神经网络，它根据 Lyα 发射线的红侧 （1216-1600 Å） 预测 1040-1600 Å 静止坐标系波长范围内未被吸收的类星体连续体。我们基于非负矩阵分解 （NMF） 系数的高斯混合模型表示开发了合成光谱。这些系数来自高分辨率、低红移 （z < 0.2） 哈勃太空望远镜/宇宙起源光谱仪 （COS） 类星体光谱。我们用相同数量的 DESI Year 5 模拟光谱补充了这个基于 COS 的合成样品。LyCAN 在测试集上表现非常出色，在 DESI 模拟样品上实现了 1.5% 的森林区域中位误差，在基于 COS 的合成样品上实现了 2.0% 的中位误差，在原始 COS 光谱上实现了 4.1% 的中位误差。LyCAN 的性能比使用相同训练集的主成分分析 （PCA） 和基于 NMF 的预测方法高出 40% 或更多。我们预测了 83,635 个 DESI Year 1 光谱在 2.1 ≤ z ≤ 4.2 的红移范围内的内禀连续谱，并对有效光学深度的演变进行了绝对测量。这是迄今为止用于测量光学深度演变的最大样本。我们将这种形式的幂律拟合到我们的测量中，发现 τ0 = （2.46 ± 0.14） × 10−3，γ = 3.62 ± 0.04。我们的结果表明，与 z = 2 附近的高分辨率地面观测特别一致，这表明 LyCAN 能够仅使用森林外的光谱信息来预测森林地区的类星体连续体。