alderaan 0.2.3

A python package for fast and robust exoplanet transit lightcurve modeling

ALDERAAN

Automated Lightcurve Detrending, Exoplanet Recovery, and Analysis of Autocorrelated Noise

ALDERAAN is a fast and reliable pipeline for processing exoplanet transit photometry. The pipeline is currently capable of handling data from the Kepler Space Telescope, but in the near future will be extended to handle data from K2 and TESS.

Detrending and transit fitting are optimized for high-fidelity measurements of [P, t0, Rp/Rs, b, T14] and for inference of dynamical transit timing variations (TTVs). Noise arising from instrumental and astrophysical sources is handled using a combination of Gaussian Processes (GP) regression and autocorrelated frequency analysis. Model sampling is performed using dynamic nested sampling.

For detailed documentation, see readthedocs.org

Installation instructions

ALDERAAN requires a complex set of dependencies in order to run. To create a conda environment capable of running the ALDERAAN pipeline, copy environment.yml from the alderaan github repository to your local machine:

curl -o ./environment.yml https://raw.githubusercontent.comefs/heads/develop/environment.yml

Then run:

conda env create -n <ENV_NAME> -f environment.yml

If <ENV_NAME> is not specified, the conda environment will be named "alderaan-env".

You can then activate your environment and safely pip install the package:

conda activate alderaan-env

then

pip install alderaan

Running the pipeline

To run the pipeline, navigate into the alderaan source directory and run the following commmand:

python alderaan/pipelines/alderaan_pipeline.py -m Kepler -t K00148 -c configs/default_config.cfg 

The flags -m (mission) -t (target) and -c (config) are required and set the pipeline run conditions.

Attribution

If you make use of alderaan in your work, please cite Gilbert, Petigura, & Entrican (2025).

Please also cite the following core dependencies:

• astropy Astropy Collaboration et al. (2022)
• batman Kreidberg (2015)
• celerite Foreman-Mackey 2018
• dynesty Speagle (2020)
• numpy Harris et al, (2020)
• PyMC3 Salvatier, Wiecki, & Fonnesbeck (2016)
• scipy Virtanen et al. (2020)