supreme-Steps to Process SOSS ObservatioNs
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: Lightcurve Fitting
A major advantage of supreme-SPOON over other available NIRISS/SOSS pipelines is the ability to carry out end-to-end reductions (uncalibrated to atmosphere spectra) without relying on intermediate outputs from the JWST DMS. Furthermore, supreme-SPOON is able to run the ATOCA extraction algorithm to explicitly model the order contamination that is known to affect SOSS observations.
The latest release of supreme-SPOON can be downloaded from PyPI by running:
pip install supreme_spoon
or the latest development version can be grabbed from GitHub:
git clone https://github.com/radicamc/supreme-spoon
cd supreme_spoon
pip install .
If you plan on using the ATOCA extraction, it would be beneficial to have the atoca development version of the official jwst pipeline. Multiple critical functionalities have not yet been merged into the master pipeline, but will be soon. This can be installed via:
pip install git+https://github.com/AntoineDarveau/jwst.git@atoca_1.6.0x
The supreme-SPOON pipeline can be run in a similar fashion to the JWST DMS, by individually calling each step.
Alternatively, Stages 1 to 3 can be run at once via the run_DMS.py script.
- Copy the
run_DMS.pyscript and therun_DMS.yamlconfig file into your working directory. - Fill out the yaml file with the appropriate inputs.
- Once happy with the input parameters, enter
python run_DMS.py run_DMS.yamlin the terminal.
To use the light curve fitting capabilities, simply follow the same procedure with the fit_lightcurves.py and .yaml files. Currently only transit light curve fits are supported, with eclipse fitting in development.
If you make use of this code in your work, please cite Radica et al. (2023) and Feinstein et al. (2022).
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 Laginja & Wakeford (2020) and Magic et al. (2015).
Lastly, you should cite the libraries upon which this code is built, namely: numpy, scipy, and astropy.