zELDA
stable

Contents:

  • Introduction
  • Installation
  • About the LyaRT data grids
  • Tutorial : Computing ideal line profiles
  • Tutorial : Computing mock line profiles
  • Tutorial : Fitting a line profile using deep learning
  • Tutorial : Fitting a line profile using Monte Carlo Markov Chains
  • Tutorial : Computing Lyman-alpha escape fractions
  • funcs module
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  • Welcome to zELDA’s documentation! 10_51_342
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Welcome to zELDA’s documentation! 10_51_342¶

Contents:

  • Introduction
    • Authors
    • Publication links:
    • Origins and motivation
  • Installation
    • Python package
    • LyaRT data grids
  • About the LyaRT data grids
    • Getting started
    • Line profile LyaRT data grids
    • Line profile grids with smaller RAM occupation
  • Tutorial : Computing ideal line profiles
    • Computing one ideal line profile
    • Computing many ideal line profile
  • Tutorial : Computing mock line profiles
    • Mocking Lyman-alpha line profiles
    • Plotting cooler line profiles
  • Tutorial : Fitting a line profile using deep learning
    • Getting started
    • Using the DNN in the un-perturbed line profile
    • Using the DNN with Monte Carlo perturbations
  • Tutorial : Fitting a line profile using Monte Carlo Markov Chains
    • Getting started
    • The MCMC anlysis
    • Tool to make corraltion plots
  • Tutorial : Computing Lyman-alpha escape fractions
    • Default computation of escape fractions
    • Deeper options on predicting the escape fraction
  • funcs module

Indices and tables¶

  • Index

Next

© Copyright 2021, Gurung-Lopez, Siddhartha. Revision f8f870ee.

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