nuance

Efficient detection of planets transiting quiet or active stars

nuance uses linear models and Gaussian processes (using the JAX-based tinygp) to simultaneously search for planetary transits while modeling correlated noises (e.g. stellar variability) in a tractable way. See the paper for more details.

When to use nuance?

To detect single or periodic transits
When correlated noises are present in the data (e.g. stellar variability or instrumental systematics)
For space-based or sparse ground-based observations
To effectively find transits in light curves from multiple instruments
To use GPUs for fast transit searches

Documentation at nuance.readthedocs.io

Example

import numpy as np
from nuance import linear_search, periodic_search, core

# linear search
epochs = time.copy()
durations = np.linspace(0.01, 0.2, 15)
ls = linear_search(time, flux, gp=gp)(epochs, durations)

# periodic search
periods = np.linspace(0.3, 5, 2000)
snr_function = jax.jit(core.snr(time, flux, gp=gp))
ps_function = periodic_search(epochs, durations, ls, snr_function)
snr, params = ps_function(periods)

t0, D, P = params[np.argmax(snr)]

Installation

nuance is written for python 3 and can be installed using pip

pip install nuance

or from sources

git clone https://github.com/lgrcia/nuance
cd nuance
pip install -e .

Citation

If you find nuance useful for your research, cite Garcia et. al 2024. The BibTeX entry for the paper is:

@ARTICLE{2024AJ....167..284G,
       author = {{Garcia}, Lionel J. and {Foreman-Mackey}, Daniel and {Murray}, Catriona A. and {Aigrain}, Suzanne and {Feliz}, Dax L. and {Pozuelos}, Francisco J.},
        title = "{nuance: Efficient Detection of Planets Transiting Active Stars}",
      journal = {\aj},
     keywords = {Exoplanet detection methods, Stellar activity, Time series analysis, Gaussian Processes regression, Computational methods, GPU computing, 489, 1580, 1916, 1930, 1965, 1969, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2024,
        month = jun,
       volume = {167},
       number = {6},
          eid = {284},
        pages = {284},
          doi = {10.3847/1538-3881/ad3cd6},
archivePrefix = {arXiv},
       eprint = {2402.06835},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024AJ....167..284G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}