supreme-SPOON

supreme-Steps to Process SOSS ObservatioNs

⭐⭐ Please note that supreme-SPOON has undergone a rebrand and future versions (>=1.4.0) will use a new name, exoTEDRF. ⭐⭐
This repository will remain in existence (though it will no longer be updated). All updates to this project will now be in the exoTEDRF repository.

supreme-SPOON is an end-to-end pipeline for NIRISS/SOSS time series observations (TSOs). The pipeline is divided into four stages:

• Stage 1: Detector Level Processing
• Stage 2: Spectroscopic Processing
• Stage 3: 1D Spectral Extraction
• Stage 4: Light Curve Fitting (optional)

Installation Instructions

The latest release of supreme-SPOON can be downloaded from PyPI by running:

pip install supreme_spoon

Depending on the operating system, the package jmespath may fail to install. In this case, run pip install jmespath, and then proceed with the supreme-SPOON installation.

The default pip installation only includes Stages 1 to 3. Stage 4 can be included via specifying the following option during installation:

pip install supreme_spoon[stage4]

Note that the radvel package may fail to build during the installation of Stage4. If so, simply run pip install cython, and then proceed with the supreme-SPOON installation as before.

The latest development version can be grabbed from GitHub (inlcludes all pipeline stages):

git clone https://github.com/radicamc/supreme-spoon
cd supreme_spoon
python setup.py install

Note that supreme-SPOON is currently compatible with python 3.10.4 and v1.12.5 of the jwst package maintained by STScI. If you wish to run a different version of jwst, certain functionalities of supreme-SPOON may not work.

Usage Instructions

The supreme-SPOON pipeline can be run in a similar fashion to jwst by individually calling each step. Alternatively, Stages 1 to 3 can be run at once via the run_DMS.py script.

1. Copy the run_DMS.py script and the run_DMS.yaml config file into your working directory.
2. Fill out the yaml file with the appropriate inputs.
3. Once happy with the input parameters, enter python run_DMS.py run_DMS.yaml in the terminal.

To use the light curve fitting capabilities (if installed), simply follow the same procedure with the fit_lightcurves.py and .yaml files.

ATOCA Caveats

supreme-SPOON has the ability to run the ATOCA extraction algorithm to explicitly model the overlap of the first and second SOSS spectral orders on the detector. ATOCA is now the default extract1d method in the STScI jwst pipeline, however, since jwst v1.9.0 it is partially broken. In order to use ATOCA, you must install jwst v1.8.5. You may also need to specifically install astropy v5.3.4 and asdf v2.15.2. All other pipeline functionalities should work with this jwst version. With jwst v1.8.5 a pipeline error stpipe.config_parser.ValidationError: Extra value 'edge_size' in root may be encountered during the JumpStep, if specifying fit_up_ramp=True. This comes from a STScI update to the JumpStep parameter file which is not backwards compatible. To circumvent this error, one can download an older version of the parameter file from the CRDS. Navigate to the niriss downdown menu, and then to pars-jumpstep. Download the file jwst_niriss_pars-jumpstep_0020.asdf. Then rename it jwst_niriss_pars-jumpstep_0081.asdf (or whichever is the most recent version) and replace the automatically downloaded parameter file in your crds_cache directory.

Citations

If you make use of this code in your work, please cite Radica et al. (2023) and Feinstein et al. (2023).

Additional Citations

If you use the ATOCA extraction algorithm, please also cite Radica et al. (2022) and Darveau-Bernier et al. (2022).

If you make use of the light curve fitting routines, also include the following citations for juliet, batman, dynesty, and Kipping et al. (2013) for the limb-darkening sampling. If you use Gaussian Processes please cite celerite, and if you use ExoTiC-LD for limb darkening priors cite Grant & Wakeford (2022). Please also see the ExoTiC-LD documentation for information on the types of stellar grids available and ensure to correctly download and cite the desired models.

Lastly, you should cite the libraries upon which this code is built, namely: numpy, scipy, astropy, and of course jwst.