transitleastsquares 1.0.11

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

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

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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.

Installation

The stable version can be installed via pip: pip install tls-package

Dependencies: Python 3, SciPy, NumPy, numba, batman, tqdm, argparse

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.

Attribution

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

@article{abc,
   author = {},
    title = {},
  journal = {},
     year = 2019,
   volume = ,
    pages = {},
   eprint = {},
      doi = {}
}

Contributing Code, Bugfixes, or Feedback

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

Copyright 2019 Michael Hippke & René Heller.