CLEDB Coronal Magnetic Field Database Inversion

Repository for CLEDB - the Coronal Line Emission DataBase inversion.

Authors: Alin Paraschiv, Thomas Schad, and Philip Judge. National Solar Observatory & High Altitude Observatory

Contact: arparaschiv "at" nso.edu

Main aim:

Invert coronal vector magnetic field products from observations of polarized light. The algorithm takes arrays of one or two sets of spectro-polarimetric Stokes IQUV observations to derive line of sight and/or full vector magnetic field products.

WARNING: For the remainder of 2024 CLEDB is in active development in preparation for supporting DKIST Cryo-NIRSP data. Issues might appear in between updates. Please get in contact if noticing weird behavior.

Applications:

Inverting magnetic field information from spectro-polarimetric solar coronal observations from instruments like DKIST Cryo-NIRSP; DL-NIRSP; MLSO COMP/UCOMP.

Documentation

1. Extensive documentation, including installation instruction, dependencies, algorithm schematics and much more is available on CLEDB.READTHEDOCS.IO A git distribution PDF build is also provided.
2. In-depth documentation for the Bash & Fortran parallel database generation module is provided in README-RUNDB.md.
3. Installation and usage on RC systems is described in README-SLURM.md.
4. This is a beta-level release. Not all functionality is implemented. TODO.md documents updates, current issues, and functions to be implemented in the near future.

System platform compatibility

1. Debian+derivatives Linux x64 -- all inversion modules are fully working.
2. RC system CentOS linux x64 -- all inversion modules are fully working. An additional binary executable is provided. May require local compiling.
3. OSX (Darwin x64) Catalina and Big Sur -- all inversion modules are fully working; One additional homebrew package required. See README-RUNDB.
4. Windows platform -- not tested.

Examples

Install the CLEDB distribution, generate databases, and update the database save location in the ctrlparams.py class, as described in the CLEDB.READTHEDOCS.IO .

The new PyCELP database generation tool is recommended. It is more precise, but requires some computational resources for calculations. A default PyCELP generated database can be found here to help get started (33Gb download). Just extract the two database folders in the CLEDB_BUILD directory and you should be set to running the examples.

Afterward, both 1-line and 2-line implementations of CLEDB can be tested with synthetic data using the two provided Jupyter notebook examples

1. test_1line.ipynb
2. test_2line_IQUV.ipynb

The test data are hosted separately. These are called by enabling the corresponding 1.a-1.e cells in the test notebooks and scripts. See the documentation for details regarding the included datafiles.

• 1.a synthetic CLE 3 dipole data.
• 1.b synthetic CLE current-sheet data will be available soon.
• 1.c Only for internal testing.
• 1.d CoMP observation data.
• 1.e CoMP doppler analysis results for the 1.d datacube.

For terminal only compute systems, the test data can be downloaded via the shell interface with the following method:

i. Load the following gdrive wrapper script into your bash window directly, or introduce it in your .bash_alias setup.

function gdrive_download () {   CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p');   wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2;   rm -rf /tmp/cookies.txt; }

ii. Download the file using its gdrive FILE_ID from the download link (1.a test data FILE_ID = 1beyDfZbm6epMne92bqlKXcgPjYI2oGRR):

gdrive_download FILE_ID local_path/local_name   (sometimes needs to be run two times to set cookies correctly!)

Note: The script versions of all tests test_1line.py and test_2line.py together with the test_cledb_slurm.sh are slurm enabled to be used for headless RC system runs. These offer the same functionality as the notebooks (from which they are directly generated from). See the dedicated README-SLURM for additional information.

Both test examples are expected to fully execute with parallel job spawning via Numba/JIT in a correct installation.

Works that fundament and support the CLEDB methodology

1. Paraschiv & Judge, SolPhys, 2022 covered the scientific justification of the algorithm, and the setup of the CLEDB inversion.
2. Judge, Casini, & Paraschiv, ApJ, 2021 discussed the importance of scattering geometry when solving for coronal magnetic fields.
3. Ali, Paraschiv, Reardon, & Judge, ApJ, 2022 performed a spectroscopic exploration of the infrared regions of emission lines available for inversion with CLEDB.
4. Dima & Schad, ApJ, 2020 discussed potential degeneracies in using certain line combinations. The one-line CLEDB inversion utilizes the methods and results described in this work.
5. Schiffmann, Brage, Judge, Paraschiv & Wang, ApJ, 2021 performed large-scale Lande g factor calculations for ions of interest and discusses degeneracies in context of their results.
6. Casini & Judge, ApJ, 1999 and Judge & Casini, ASP proc., 2001 described the theoretical line formation process implemented in CLE, the coronal forward-synthesis code that is currently utilized by CLEDB.