TSARDI: a Machine Learning data rejection algorithm for transiting exoplanet light curves

We present TSARDI, an efficient rejection algorithm designed to improve the transit detection efficiency in data collected by large scale surveys. TSARDI is based on the Machine Learning clustering algorithm DBSCAN, and its purpose is to serve as a robust and adaptable filter aiming to identify unwanted noise points left over from data detrending processes. TSARDI is an unsupervised method, which can treat each light curve individually; there is no need of previous knowledge of any other field light curves. We conduct a simulated transit search by injecting planets on real data obtained by the QES project and show that TSARDI leads to an overall transit detection efficiency increase of $\sim$11\%, compared to results obtained from the same sample, but using a standard sigma-clip algorithm. For the brighter end of our sample (host star magnitude < 12), TSARDI achieves a detection efficiency of $\sim$80\% of injected planets. While our algorithm has been developed primarily for the field of exoplanets, it is easily adaptable and extendable for use in any time series.

[1]  Marc Ollivier,et al.  The SARS algorithm: detrending CoRoT light curves with Sysrem using simultaneous external parameters , 2010, 1003.0427.

[2]  Keivan G. Stassun,et al.  KELT-3b: A HOT JUPITER TRANSITING A V = 9.8 LATE-F STAR , 2012, 1211.1031.

[3]  A. Lähteenmäki,et al.  Cluster analyses of gigahertz-peaked spectrum sources with self-organising maps , 2008 .

[4]  E. Bachelet,et al.  SIDRA: a blind algorithm for signal detection in photometric surveys , 2015, 1511.03456.

[5]  R. W. Noyes,et al.  A trend filtering algorithm for wide-field variability surveys , 2004 .

[6]  P. Tenenbaum,et al.  AUTOMATIC CLASSIFICATION OF KEPLER PLANETARY TRANSIT CANDIDATES , 2014, 1408.1496.

[7]  G. Bruce Berriman,et al.  Astrophysics Source Code Library , 2012, ArXiv.

[8]  A. Pál.  Properties of analytic transit light-curve models , 2008 .

[9]  David J Armstrong,et al.  Transit shapes and self-organizing maps as a tool for ranking planetary candidates: application to Kepler and K2 , 2016, 1611.01968.

[10]  G. Kov'acs,et al.  A box-fitting algorithm in the search for periodic transits , 2002, astro-ph/0206099.

[11]  David J Armstrong,et al.  K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4 , 2015, 1512.01246.

[12]  P. Dubath,et al.  ASTErIsM: application of topometric clustering algorithms in automatic galaxy detection and classification , 2016, 1609.06728.

[13]  R. J. Brunner,et al.  Exhausting the Information: Novel Bayesian Combination of Photometric Redshift PDFs , 2014, 1403.0044.

[14]  An algorithm for correcting CoRoT raw light curves , 2010, 1008.0300.

[15]  R. G. West,et al.  Efficient identification of exoplanetary transit candidates from SuperWASP light curves , 2007, 0707.0417.

[16]  David J Armstrong,et al.  Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS , 2018, 1805.07089.

[17]  David Charbonneau,et al.  TrES-1: The Transiting Planet of a Bright K0 V Star , 2004 .

[18]  B. Enoch,et al.  The WASP Project and the SuperWASP Cameras , 2006, astro-ph/0608454.

[19]  S. C. C. Barros,et al.  New planetary and eclipsing binary candidates from campaigns 1−6 of the K2 mission , 2016, 1607.02339.

[20]  D. Mislis,et al.  The DOHA algorithm: A new recipe for cotrending large-scale transiting exoplanet survey light curves , 2016, 1611.07733.

[21]  Tsevi Mazeh,et al.  Correcting systematic effects in a large set of photometric light curves , 2005, astro-ph/0502056.

[22]  Carl J. Grillmair,et al.  AUTOMATED CLASSIFICATION OF PERIODIC VARIABLE STARS DETECTED BY THE WIDE-FIELD INFRARED SURVEY EXPLORER , 2014, 1402.0125.

[23]  K. Stanek,et al.  Wide‐Field Millimagnitude Photometry with the HAT: A Tool for Extrasolar Planet Detection , 2004, astro-ph/0401219.