Modeling Physical Processes at Galactic Scales and Above

Gnedin, Nickolay Y.

doi:10.1007/978-3-662-47890-5_1

Cited by 1 publication

(1 citation statement)

References 96 publications

(134 reference statements)

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“…where 𝜎 0 = 4.5 × 10 −18 cm 2 is the cross-section for resonant Ly𝛼 absorption, 𝑏(𝑇) = (2𝑘𝑇/𝑚 𝐻 ) 1/2 is the Doppler parameter, 𝑚 𝐻 is the mass of hydrogen atom, and 𝑐 is the speed of light (Gnedin 2016).…”

Section: Synthetic Spectramentioning

confidence: 99%

Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning

Veiga¹,

Meng²,

Gnedin³

et al. 2021

Preprint

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We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra. State-of-the-art cosmological simulations of structure formation are used to generate a large synthetic dataset of line-of-sight absorption spectra paired with 1-dimensional fluid quantities along the same line-of-sight, such as the total density of matter and the density of neutral atomic hydrogen. With this dataset, we build a series of data-driven models to predict the power spectrum of total matter density. We are able to produce models which yield reconstruction to accuracy of about 1% for wavelengths 𝑘 ≤ 2 ℎ Mpc −1 , while the error increases at larger 𝑘. We show the size of data sample required to reach a particular error rate, giving a sense of how much data is necessary to reach a desired accuracy. This work provides a foundation for developing methods to analyse very large upcoming datasets with the next-generation observational facilities.

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Section: Synthetic Spectramentioning

confidence: 99%