DREAMS_DM_speed_distributions

This repository is for the paper The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Speed Distributions by Ethan Lilie. It is associated with manuscript number AAS72250R1. Code used to produce dark matter velocity distributions from DREAMS CDM simulations. One tutorial showing how to get the velocity distribution using kernel density estimation, and one tutorial for using the velocity distribution with the NeHOD emulator. Reusing this material requires Jupyter Lab, Numpy, Scipy, Pytorch, Pytorch Lightning, and Pandas. KDE_Tutorial is a Jupyter notebook that shows how to start from simulation data (positions and speeds of simulations particles) and to get an estimation of the local speed distribution of the particles. Emulator_Tutorial is a Jupyter notebook that can be used after having generated speed distributions (such as after using KDE_Tutorial). This tutorial shows how to start from an array of halo mass, e_w, κ_w, and ϵ_f,high and output an emulated velocity distributions using the NeHOD normalizing flows emulator. This requires the training data, KDE histograms from simulations, and outputs emulated KDEs. Varied_100000_PARAMS is a textfile contains for each of the 10^6 histogram the list of varied baryonic in the format np.array(3,100000) where the 3 rows correspond to e_w, κ_w, and ϵ_f,high. This would be an input of Emulator_tutorial. Varied_100000_HISTS is a textfile that containts the 10^6 histogrammed speed distributions used in [] in the format of np.array(63,100000) Where the first three rows correspond to stellar mass, mass enclosed at 8 kpc, and the radius where the mass enclosed matches the mass enclosed at 8 kpc of the Milky Way. The last 60 rows are the height of the histogram for all 60 bins from speeds of 0 to 650 km/s. This is an example output of Emulator_Tutorial.