TensorFlow Hydrodynamics Analysis for Ly-$\alpha$ Simulations

TL;DR

This paper introduces THALAS, a fully differentiable Python tool for analyzing hydrodynamical simulations of the Lyman-Alpha forest, enabling advanced cosmological inference and data reconstruction.

Contribution

The paper presents THALAS, a novel, fully differentiable Python program that maps baryon fields to Ly$ ext{alpha}$ optical depth fields, facilitating new analysis methods for hydrodynamical simulations.

Findings

Demonstrated THALAS's ability to invert Ly$ ext{alpha}$ optical depth to real-space dark matter density.

Showcased applications in cosmological and tomographic analyses of Lyman Alpha forest data.

Enabled potential for improved cosmological inference through differentiable modeling.

Abstract

We introduce the Python program THALAS (TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations), which maps baryon fields (baryon density, temperature, and velocity) to Ly$\alpha$ optical depth fields in both real space and redshift space. Unlike previous Ly$\alpha$ codes, THALAS is fully differentiable, enabling a wide variety of potential applications for general analysis of hydrodynamical simulations and cosmological inference. To demonstrate THALAS's capabilities, we apply it to the Ly$\alpha$ forest inversion problem: given a Ly$\alpha$ optical depth field, we reconstruct the corresponding real-space dark matter density field. Such applications are relevant to both cosmological and three-dimensional tomographic analyses of Lyman Alpha forest data.