Precise time series photometry for the Kepler-2.0 mission

By Suzanne Aigrain, S. Hodgkin, M. Irwin, J. Lewis, Stephen J. Roberts. 2014

94 citations recorded by the source corpus.

The recently approved NASA K2 mission has the potential to multiply by an order of magnitude the number of short-period transiting planets found by Kepler around bright and low-mass stars, and to revolutionise our understanding of stellar variability in open clusters. However, the data processing is made more challenging by the reduced pointing accuracy of the satellite, which has only two functioning reaction wheels. We present a new method to extract precise light curves from K2 data, combining list-driven, soft-edged aperture photometry with a star-by-star correction of systematic eects associated with the drift in the roll-angle of the satellite about its boresight. The systematics are modelled simultaneously with the stars’ intrinsic variability using a semi-parametric Gaussian process model. We test this method on a week of data collected during an engineering test in January 2014, perform checks to verify that our method does not alter intrinsic variability signals, and compute the precision as a function of magnitude on long-cadence (30-min) and planetary transit (2.5-hour) timescales. In both cases, we reach photometric precisions close to the precision reached during the nominal Kepler mission for stars fainter than 12 th magnitude, and between 40 and 80 parts per million for brighter stars. These results conrm the bright prospects for planet detection and characterisation, asteroseismology and stellar variability studies with K2. Finally, we perform a basic transit search on the light curves, detecting 2 bona de transit-like events, 7 detached eclipsing binaries and 13 classical variables.

Subfields: Astronomy and Astrophysics, Instrumentation

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Citations in the corpus

A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA (2015, methodology): “Following Vanderburg & Johnson (2014), we choose the aperture size with the minimum CDPP (Christiansen et al. 2012) with a 6 hour window.3 All previous methods for analyzing K2 data involve some sort of “correction” or “de- trending” step based on measurements of the pointing of the spacecraft (Vand”
The nature of the TRAPPIST-1 exoplanets. (2018, methodology): “We used an additive kernel with separate spatial, time and white noise components (Aigrain et al. 2015, 2016; Luger et al. 2017): kxy(xi, yi, x j, y j) = Axy exp − (xi − x j)2L2x − (yi − y j) 2 L2y (1) kxy(ti, t j) = At exp [ − (ti − t j)2 L2t ] (2) Ki j = kxy(xi, yi, x j, y j) + kt(ti, t j) +…”
PLANETARY CANDIDATES FROM THE FIRST YEAR OF THE K2 MISSION (2015, background): “Since then, a variety of new K2 data analysis techniques and planet detections have been presented (Aigrain et al. 2015; Armstrong et al. 2015a,b; LaCourse et al. 2015; Foreman-Mackey et al. 2015; Montet et al. 2015; Sanchis-Ojeda et al. 2015; Huang et al. 2015a,b; Angus et al. 2015; Lund et al.…”
Optimized transit detection algorithm to search for periodic transits of small planets (2019, methodology): “…Savitzky & Golay (1964) filter (used by Gilliland et al. 2011), the median filter (Tal-Or et al. 2013), polynomial filters (Gautier et al. 2012; Rodenbeck et al. 2018), the Cosine Filtering with Autocorrelation Minimization (CoFiAM, Kipping et al. 2013), and Gaussian process (Aigrain et al. 2015).”
ROTATION IN THE PLEIADES WITH K2. I. DATA AND FIRST RESULTS (2016, methodology): “(3) A version using a semiparametric Gaussian process model used by Aigrain et al. (2015, 2016).”
THE K2 ECLIPTIC PLANE INPUT CATALOG (EPIC) AND STELLAR CLASSIFICATIONS OF 138,600 TARGETS IN CAMPAIGNS 1–8 (2015, background): “…to account for systematic variability caused by the spacecraft thruster firings are continuously being developed (Vanderburg & Johnson 2014; Aigrain et al. 2015; Angus et al. 2016; Huang et al. 2015; Lund et al. 2015) and have demonstrated that the photometric precision of K2 is at least…”
EVEREST: PIXEL LEVEL DECORRELATION OF K2 LIGHT CURVES (2016, methodology): “Finally, the K2SC pipeline (Aigrain et al. 2015, 2016) and the pipeline of Crossfield et al. (2015) both employ a Gaussian process (GP) to remove instrumental noise, using the X and Y coordinates of the target star as the regressors to derive a model for the instrumental systematics.”
K2 Variable Catalogue: Variable Stars and Eclipsing Binaries in K2 Campaigns 1 and 0 (2015, methodology): “We are aware of one other published method for K2 data detrending, that of Aigrain et al. (2015).”