Code to calculate the spherically averaged power spectrum for [CII] and CO(1-0) lines
Some warnings/advice:
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As of now, the code calculates the clustering and shot-noise spherically averaged power spectrum for [CII] and CO(1-0) lines using some recipes (SFR to L_{[CII]} or L_{CO}) available. The SFR required is calculated using the best fit parameterization of Maniyar 2021 et al. (https://arxiv.org/pdf/2006.16329.pdf) which was developed for a CIB halo model.
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In the current version of the code, we do not do a full halo-model calculation. We just divide the power spectra in the clustering and shot noise terms. However, it is pretty straightforward to implement the halo model formalism and get the power spectra as 1-halo, 2-halo, and shot noise terms.
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It has to be noted that currently the code is written such that it calculates P(k, z) for several redshifts at the same time. Thus, if you give only one redshift value as an input, the clustering power spectrum will come out with the shape (len(k), 1). On the other hand, shot noise is just a number for every redshift and not an array.
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If you want to run the code several times, or in a MCMC run, or just in general want to speed up the calculations, pre-calculate and store the halo mass function for a given mass and redshift range and then just interpolate for the required mass and redshift ranges. Same argument applicable to halo bias and especially the Fourier transform of the NFW profile if you want to use the full halo model formalism.
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Requires numpy, scipy, and astropy for calculations and matplotlib for plotting purposes. scipy and astropy dependancies are redundant and code can actually be written fully using numpy if required.
You can just clone the repository, and run driver.py file:
python driver.py
If you use this code in a publication, please cite https://arxiv.org/abs/2006.16329. If you have any comments, suggestions or questions, please do not hesitate to contact me: abhishek.maniyar@nyu.edu