47 citations · 53 across the 5 of their papers we have counts for
5 papers
A Measurement of the Thermal and Ionization State of the IGM at
Teng Hu, Vikram Khaire, Joseph F. Hennawi +4
We apply a machine-learning-based inference method that exploits the joint Doppler parameter-column density (b-NHI) distribution from Lya forest decomposition to measure the therma…
LyαNNA II: Field-level inference with noisy Lyα forest spectra
Parth Nayak, Michael Walther, Daniel Gruen
Deep learning (DL) has been shown to outperform traditional, human-defined summary statistics of the Lyα forest in constraining key astrophysical and cosmological parameters owing…
Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman- forest
S. Chang, P. Nayak, M. Walther +1
In order to compress and more easily interpret Lyman- forest (LyF) datasets, summary statistics, e.g. the power spectrum, are commonly used. However, such summaries unavoidab…
LyNNA: A Deep Learning Field-level Inference Machine for the Lyman- Forest
Parth Nayak, Michael Walther, Daniel Gruen +1
The inference of astrophysical and cosmological properties from the Lyman- forest conventionally relies on summary statistics of the transmission field that carry useful but lim…
Constraining Reionization with the Lyman- Forest Power Spectrum: the Outlook after Planck
J. Oñorbe, J. F. Hennawi, Z. Lukić +1
The latest measurements of CMB electron scattering optical depth reported by Planck significantly reduces the allowed space of HI reionization models, pointing toward a later endin…