Skip to main content

An optimized transit-fitting algorithm to search for periodic transits of small planets

Project description

Logo

An optimized transit-fitting algorithm to search for periodic transits of small planets

Image Image Image Image Image Build Status Image

Motivation

We present a new method to detect planetary transits from time-series photometry, the Transit Least Squares (TLS) algorithm. While the commonly used Box Least Squares (BLS, Kovács et al. 2002) algorithm searches for rectangular signals in stellar light curves, TLS searches for transit-like features with stellar limb-darkening and including the effects of planetary ingress and egress. Moreover, TLS analyses the entire, unbinned data of the phase-folded light curve. These improvements yield a ~10 % higher detection efficiency (and similar false alarm rates) compared to BLS. The higher detection efficiency of our freely available Python implementation comes at the cost of higher computational load, which we partly compensate by applying an optimized period sampling and transit duration sampling, constrained to the physically plausible range. A typical Kepler K2 light curve, worth of 90 d of observations at a cadence of 30 min, can be searched with TLS in 10 seconds real time on a standard laptop computer, just as with BLS.

image

Installation

The stable version can be installed via pip: pip install transitleastsquares

To get the latest version, use

git clone https://github.com/hippke/tls
cd tls
python setup.py install

Dependencies: Python 3, NumPy, numba, batman-package, tqdm, optional: argparse (for the command line version), astroquery (for LD and stellar density priors from Kepler K1, K2, and TESS).

Getting started

Here is a short animation of a real search for planets in Kepler K2 data. For more examples, have a look at the tutorials and the documentation.

image

Attribution

Please cite Hippke & Heller (2019, A&A accepted) if you find this code useful in your research. The BibTeX entry for the paper is:

@ARTICLE{2019arXiv190102015H,
       author = {{Hippke}, Michael and {Heller}, Ren{\'e}},
        title = "{Transit Least Squares: An optimized transit detection algorithm to search for periodic transits of small planets}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2019,
        month = Jan,
          eid = {arXiv:1901.02015},
        pages = {arXiv:1901.02015},
archivePrefix = {arXiv},
       eprint = {1901.02015},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190102015H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Contributing Code, Bugfixes, or Feedback

We welcome and encourage contributions. If you have any trouble, open an issue.

Copyright 2019 Michael Hippke & René Heller.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

transitleastsquares-1.0.17.tar.gz (43.5 kB view details)

Uploaded Source

Built Distribution

transitleastsquares-1.0.17-py3-none-any.whl (45.7 kB view details)

Uploaded Python 3

File details

Details for the file transitleastsquares-1.0.17.tar.gz.

File metadata

  • Download URL: transitleastsquares-1.0.17.tar.gz
  • Upload date:
  • Size: 43.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.7.2

File hashes

Hashes for transitleastsquares-1.0.17.tar.gz
Algorithm Hash digest
SHA256 0b47b1671d7d9c582416a409e4a0df727eee48ef3d2ac598b7ecec98ae5dd9fb
MD5 2aab57f65fcd745036642cb6cb353d1b
BLAKE2b-256 fcd8f1667fd061c12c24760dc51d25929650442420684d8156b7e74391f43da4

See more details on using hashes here.

File details

Details for the file transitleastsquares-1.0.17-py3-none-any.whl.

File metadata

  • Download URL: transitleastsquares-1.0.17-py3-none-any.whl
  • Upload date:
  • Size: 45.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.7.2

File hashes

Hashes for transitleastsquares-1.0.17-py3-none-any.whl
Algorithm Hash digest
SHA256 17336939b439c385391579951216ee8a4846353e3ce53e716eb1e98b33021966
MD5 3f522097370d55c3342271d526a0c8c8
BLAKE2b-256 da5b448a75c143fec38d03a7ddd484a31685384326bc6bea53e933d2e126101f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page