*Different realizations of small scales at 12 arcminutes in the second column.
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Simulate stochastic, polarized(QU), non-Gaussian thermal dust emission at 353GHz up to 3 arcminutes.
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plus version of https://github.com/ai4cmb/ForSE.
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If you want to use the generated maps directly, we also offer some realizations of maps at url...
- Namaster: to compute power spectra to normalize the small scales from neural networks
- reproject: only needed to perform projection from Healpix maps to flat patches and vice versa
- numba: only needed to accelearte the calculation of Minkowski functionals for a given patch
We assume you alrealy have your own python virtual environment.
The first thing to do is to install the dependencies and the main difficulty is to install the Namaster package, which has its own dependencies. You can install it with conda and if you want to install Namaster dependencies from source codes, we prepared a install_dependencies.sh file for you.
With all dependencies solved, you have two ways to install this package.
Download the source code, then
(venv) $ python -m pip install . --user
(venv) $ python -m pip install ForSEplus --user
The zipped complete ancillary data can be downloaded here (4GB, 9.4GB after decompression). Then decompress the files into a directory, whose path should be given to dir_data when running the pipeline. If you are on NERSC, you can use dir_data = /pscratch/sd/j/jianyao/ForSE_plus_data_32/, which I already open the permission to all users on NERSC.
Once installed, import the forseplus as:
from ForSEplus.forseplus_class import forseplus
Then intialize a instance to generate maps:
fp = forseplus(dir_data = '/pscratch/sd/j/jianyao/ForSE_plus_data_32/',
return_12 = True,
go_3 = True,
correct_EB = False,
seed = None)
You can choose to return sotchastic maps at 12 arcmin only (return_12 = True, go_3 = False), or maps at 3 arcmin only (return_12 = False, go_3 = True), or both (return_12 = True, go_3 = True), though in any case maps at 12 arcmin will be generated since 12arcmin maps will be the input to generate maps at 3arcmin.
Then you can run:
maps_12amin, maps_3amin = fp.run()
to obtain QU Maps, which will be in uK_CMB units. Returned QU maps at 12amin will be (2, 12*2048**2), 3amin will be (2, 12*4096**2).
If set correct_EB = True, it will apply the E/B ratio correction proposed in Yao et al. to artificially tune the Cl_EE/Cl_BB = 2 for the generated small scales. Otherwise, Cl_EE/Cl_BB = 1. Refer to Section 4.1 of Yao et al. for more details.
seed parameter defines the random seed to generate random component which are added to the fixed, observed large scales. If you want to generate many realizations, just put the fp.run() inside a loop and make sure seed = None.
Memory needed (Peak memory) and time cost (with correct_EB = False, test on Perlmutter Jupyter login node)
| Case | CPU | GPU | Time |
|---|---|---|---|
| only 12amin | 16GB | 10GB | ~15 secs |
| also go to 3amin | 63.62GB* | 18GB | ~5 mins |
* This number is after a careful optimization which doesn't exceed the memory limit (64GB) on Perlmutter login node:-). However if you have other notebooks running, there will be a 'kernel restarting' error caused by the memory limit, so you may open an Exclusive GPU node or submit the job to a compute node (which of course needs allocation hours, which is a bit troublesome(っ◞‸◟ c), but we will continue to optimize the memory usage during the running).
@ARTICLE{2024A&A...686A.290Y,
author = {{Yao}, Jian and {Krachmalnicoff}, Nicoletta and {Foschi}, Marianna and {Puglisi}, Giuseppe and {Baccigalupi}, Carlo},
title = "{FORSE+: Simulating non-Gaussian CMB foregrounds at 3 arcmin in a stochastic way based on a generative adversarial network}",
journal = {\aap},
keywords = {methods: data analysis, cosmic background radiation, diffuse radiation, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2024,
month = jun,
volume = {686},
eid = {A290},
pages = {A290},
doi = {10.1051/0004-6361/202449827},
archivePrefix = {arXiv},
eprint = {2406.14519},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024A&A...686A.290Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2021ApJ...911...42K,
author = {{Krachmalnicoff}, Nicoletta and {Puglisi}, Giuseppe},
title = "{ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales}",
journal = {\apj},
keywords = {Cosmic microwave background radiation, Neural networks, Diffuse radiation, 322, 1933, 383, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2021,
month = apr,
volume = {911},
number = {1},
eid = {42},
pages = {42},
doi = {10.3847/1538-4357/abe71c},
archivePrefix = {arXiv},
eprint = {2011.02221},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021ApJ...911...42K},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}