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The Python Sky Model: software for simulating the Galactic microwave sky

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 Added by Ben Thorne
 Publication date 2016
  fields Physics
and research's language is English




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We present a numerical code to simulate maps of Galactic emission in intensity and polarization at microwave frequencies, aiding in the design of Cosmic Microwave Background experiments. This Python code builds on existing efforts to simulate the sky by providing an easy-to-use interface and is based on publicly available data from the WMAP and Planck satellite missions. We simulate synchrotron, thermal dust, free-free, and anomalous microwave emission over the whole sky, in addition to the Cosmic Microwave Background, and include a set of alternative prescriptions for the frequency dependence of each component that are consistent with current data. We also present a prescription for adding small-scale realizations of these components at resolutions greater than current all-sky measurements. The code is available at https://github.com/bthorne93/PySM_public.



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The Python Sky Model (PySM) is a Python package used by Cosmic Microwave Background (CMB) experiments to simulate maps, in HEALPix pixelization, of the various diffuse astrophysical components of Galactic emission relevant at CMB frequencies (i.e. dust, synchrotron, free-free and Anomalous Microwave Emission), as well as the CMB itself. These maps may be integrated over a given instrument bandpass and smoothed with a given instrument beam. PySM 2, released in 2016, has become the de-facto standard for simulating Galactic emission, for example it is used by CMB-S4, Simons Observatory, LiteBird, PICO, CLASS, POLARBEAR and other CMB experiments, as shown by the 80+ citations of the PySM 2 publication. As the resolution of upcoming experiments increases, the PySM 2 software has started to show some limitations, the solution to these issues was to reimplement PySM from scratch focusing on these features: reimplement all the models with the numba Just-In-Time compiler for Python to reduce memory overhead and optimize performance; use MPI through mpi4py to coordinate execution of PySM 3 across multiple nodes and rely on libsharp, for distributed spherical harmonic transforms; employ the data utilities infrastructure provided by astropy to download the input templates and cache them when requested. At this stage we strive to maintain full compatibility with PySM 2, therefore we implement the exact same astrophysical emission models with the same naming scheme. In the extensive test suite we compare the output of each PySM 3 model with the results obtained by PySM 2.
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