New Measurements of the Lyα Forest Continuum and Effective Optical Depth with LyCAN and DESI Y1 Data

New Measurements of the Lyα Forest Continuum and Effective Optical Depth with LyCAN and DESI Y1 Data

We present the Ly α 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 Ly α emission line (1216–1600 Å). We developed synthetic spectra based on a Gaussian...

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Main Authors: Wynne Turner, Paul Martini, Naim Göksel Karaçaylı, J. Aguilar, S. Ahlen, D. Brooks, T. Claybaugh, A. de la Macorra, A. Dey, P. Doel, K. Fanning, J . E. Forero-Romero, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, G. Gutierrez, J. Guy, H. K. Herrera-Alcantar, K. Honscheid, S. Juneau, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, A. Meisner, R. Miquel, J. Moustakas, E. Mueller, A. Muñoz-Gutiérrez, A. D. Myers, J. Nie, G. Niz, C. Poppett, F. Prada, M. Rezaie, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, Michael F. Schubnell, H. Seo, D. Sprayberry, G. Tarlé, B. A. Weaver, H. Zou
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:The Astrophysical Journal
Subjects:
Convolutional neural networks
Cosmology
Dark energy
Intergalactic medium
Large-scale structure of the universe
Lyman alpha forest
Online Access:https://doi.org/10.3847/1538-4357/ad8239
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Summary:We present the Ly α 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 Ly α 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 $\tau {(z)={\tau }_{0}(1+z)}^{\gamma }$ to our measurements and find τ _0 = (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.
ISSN:1538-4357

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