Lya-zelda 0.0.5

Fast Lyman alpha Radiative Transfer for everyone!

Introduction

zELDA, a code to understand Lyman-alpha emission.

Authors

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| Siddhartha Gurung Lopez | Max Gronke | Alvaro Orsi | Silvia Bonoli | Shun Saito

Publication links:

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zELDA paper:

| ADS : ??? | arXiv : ??? | | zELDA is based on its previous version, FLaREON. Please, if you used zELDA in your project, cite also FLaREON: | | ADS : http://adsabs.harvard.edu/abs/2018arXiv181109630G | arXiv : https://arxiv.org/abs/1811.09630

Documentation

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You can find all the necessay information for installation and tutorials here:

| https://zelda.readthedocs.io/en/latest/

Origins and motivation

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The main goal of zELDA is to provide the scientific community with a common tool to analyze and model Lyman-alpha line profiles.

zELDA is a publicly available python package based on a RTMC (Orsi et al. 2012) and FLaREON able to predict large amounts of Lyman alpha line profiles and esc ape fractions with high accuracy. We designed this code hoping that it helps researches all over the wolrd to get a better understanding of the Universe. In particu lar zELDA is divided in two main functionalites:

• Mocking Lyman-alpha line profiles. Due to the Lyman alpha Radiative Transfer large complexity, the efforts of understanding it moved from pure analytic studi es to the so-called radiative transfer Monte Carlo (RTMC) codes that simulate Lyman alpha photons in arbitrary gas geometries. These codes provide useful informatio n about the fraction of photons that manage to escape and the resulting Lyman alpha line profiles. The RTMC approach has shown to reproduce the observed properties of Lyman-alpha emitters. zELDA constains several data grids of LyaRT, the RTMC described in Orsi et al. 2012 (https://github.com/aaorsi/LyaRT), from which Lyman -alpha line profiles are computed using lineal interpolation. This methodology allow us to predict line profiles with a high accuracy with a low compitational cost. In fact, the used by zELDA to predict a single line profiles y usually eight orders of magnitud smaller than the full radiative transfer analysis done by LyaRT . Additionally, in order to mock observed Lyman-alpha spectrum, zELDA also includes rutines to mimick the artifacts induced by obsevations in the line profiles, s uch a finite spectral resolution or the wavelegnth binning.

• Fitting observed Lyman-alpha line profiles. The main update from FLaREON to zELDA is the inclussion of several fitting algotirhms to model observed Lyman -alhpa line profiles. On the basics, zELDA uses mock Lyman-alpha line profiles to fit observed espectrums in two main phasions :

• Monte Carlo Markov Chain : This is the most classic approach taken in the literaute (e.g. Gronke et al. 2017). zELDA implementation is power by the publi c code emcee (https://emcee.readthedocs.io/en/stable/) by Daniel Foreman-Mackey et al. (2013).

• Deep learning : zELDA is the first open source code that uses machine learning to fit Lyman-alpha line profiles. zELDA includes some trained deep neura l networks that predicts the best inflow/outflow model and redshift for a given observed line profile. This approach is about 3 orders of maginutd faster than the M CMC analysis and provides similar accuracies. This methodology will prove crutial in the upcoming years when tens of thousands of Lyman-alpha line profiles will be measure by instruments such as the James Webb Space Telescope. The neural network engine powering zELDA is scikitlearn (https://scikit-learn.org/stable/).