nuance

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

Example

import numpy as np
from nuance import linearsearch, periodicsearch, 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))
psfunction = periodicsearch(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}
}