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PINION (Physics-Informed neural Network for reIONization) predicts the complete 4-D hydrogen fraction evolution from the smoothed gas and mass density fields from pre-computed N-body simulations. Trained on C2-Ray simulation outputs with a physics constraint on the reionization chemistry equation, PINION accurately predicts the entire reionization history between z = 6 and 12 with only five redshift snapshots and a propagation mask as a simplistic approximation of the ionizing photon mean free path. The network's predictions are in good agreement with simulation to redshift z > 7, though the oversimplified propagation mask degrades the network's accuracy for z < 7.
pyC2Ray updates C2-Ray (ascl:2312.022), an astrophysical radiative transfer code used to simulate the Epoch of Reionization (EoR). pyC2Ray includes a new raytracing method, ASORA, developed for GPUs, and provides a Python interface for customizable use of the code. The core features of C2-Ray, written in Fortran90, are wrapped using f2py as a Python extension module, while the raytracing library ASORA is implemented in C++ using CUDA. Both are native Python C-extensions and can be directly accessed from any Python script.
SubgridClumping derives the parameters for the global, in-homogeneous and stochastic clumping model and then computes the clumping factor for large low-resolution N-body simulations smoothed on a regular grid. Written for the CUBEP3M simulation, the package contains two main modules. The first derives the three clumping model parameters for a given small high-resolution simulation; the second computes a clumping factor cube (same mesh-size as input) for the three models for the given density field of a large low-resolution simulation.
C2-Ray3Dm performs time-dependent photo-ionization calculations for 3D multiple sources, and for hydrogen only. Based on C2-Ray (ascl:2312.022), it runs under both MPI and OpenMP. The length of subroutines has been reduced to make the code more manageable and easier to read.