The current version of the SUPERBLINK proper motion catalog lists stars with proper motions µ > 40 mas yr−1 and visual magnitudes V < 20 over the entire sky north of Decl. = −30; this area includes all fields observable in K2 (L´epine 2005; L´epine & Gaidos 2011). Lists of nearby stars with estimated distances d < 100pc were assembled from this catalog, and proposed as targets of interest in the first calls forK2 targets. Several thousand SUPERBLINK stars were thus deliberately selected for K2 monitoring as part of programs aimed at monitoring nearby M dwarfs (Crossfield et al. 2016), as well as stars with very large transverse motions assumed to be old stars from the old disk population or from the Galactic halo. Additional SUPERBLINK stars were selected as targets for K2 after having been proposed by other teams based on different selection criteria, whether or not they were known to be high proper motion stars by those teams. To identify all the high proper motion stars that were observed during the K2 mission, we have cross-matched the entire SUPERBLINK catalog with the final target lists for K2 campaigns 0-15. This was done by matching the coordinates in the SUPERBLINK catalog against the coordinates provided in the K2 target lists available online. 1 We used a search radius of 5 arcsec
to find matches which identified 58,484 high proper motion SUPERBLINK-K2 (hereafter SBK2) stars monitored by Kepler in the initial K2 campaigns. Table 2.1 lists the total
number of targets of all types (K2), along with the total number of SUPERBLINK stars (SBK2) monitored in each campaign. Overall, the SBK2 stars represent ∼20% of all the stars monitored byK2.
Table 2.1: Number of High Proper Motion Targets inK2
C00-C15
Campaign K2 SBK2 SUPERBLINK Fraction
0 7757 693 8.9 1 21647 6455 29.8 2 13351 1492 11.2 3 16348 2654 16.2 4 8634 3986 46.2 5 25137 3781 15.0 6 28288 6542 23.2 7 13260 2027 15.3 8 23564 4411 18.7 10 27170 5417 19.9 11 13607 2283 16.7 12 28088 5466 19.5 13 21367 2957 13.8 14 29897 5032 16.8 15 23278 5288 22.7
Figure 2.1 plots the positions on the sky of these 58,484 SBK2 targets fromK2 campaigns 0-15. The black points represent all SBK2 targets, the red circles indicate the fast rotators identified in the present study (see Chapter 3). In both the figure and Table 2.1, it is apparent that there are more SUPERBLINK stars in campaigns 1, 4, 5, 6, and 8 compared to 0, 2, 3, and 7. There are a few reasons for the varying levels of targets per field. Fewer targets were observed in C0 as the mission was still being tested and fields C02 and C07 contained a smaller number of targets overall as these fields lie near the Galactic plane,
Figure 2.1 Positions of all 58,484 SBK2 targets in campaigns 0-15. The black points represent all SBK2 targets, the red circles represent the 1,113 fast rotators (Prot ≤ 4 days) identified in our search, see Section 2.2. Several of the campaigns targeted nearby clusters of stars, e.g. C04 the Hyades and Pleiades, C05 the Beehive, C13 the Hyades, and C15 the star forming region in Scorpius. We note that two of the CCDs were disabled in C00-C11. After C11, a third chip was lost. Thee disabled chips are seen as blank spaces in the field of view.
where high proper motion stars are found in smaller numbers. Ultimately, large numbers of SUPERBLINK stars were monitored in each campaign, and this was due in large part to the interest in M-dwarfs among the exoplanet community. A planet orbiting an M dwarf produces a large wobble in radial velocity. Since it is easier to obtain high SNR spectra of bright, nearby M dwarfs, the SUPERBLINK stars are quite popular targets for observation. We utilize the publicly availableK2 light curves from Andrew Vanderburg at the Harvard- Smithsonian Center for Astrophysics (CfA).2The reduction begins with aperture photometry to produce a raw light curve, see Figure 2.2. The raw photometry results in a zig-zag pattern created by the drift of the telescope and the firing of the thrusters whenever the spacecraft has drifted beyond a critical limit which usually occurs every 6 hours.
Figure 2.2 An example of the zag-zag pattern in the raw photometry of the K2 data. This occurs due to the changes in pixel sensitivity as the star drifts across the CCD. The jump occurs when the thrusters fire to point the telescope back to the center of the field of view, which occurs approx. every 6 hours.
This pattern is corrected for using a process called “self-flat fielding” described in detail in Vanderburg & Johnson (2014). In short, they fit a basis spline in 1.5 day long slices of the light curve. They iteratively fit and reject 3σ outliers until convergence. This spline fitting and rejection routine usually require 5 iterations. This process improves the raw photometry
by a factor of ∼4. They also find a precision dependence based on location on the CCD with the worst precision occurring at the edges of the camera’s field of view. The technique ultimately removes most of the artifacts caused by the spacecraft drift, and brings the quality of the K2 data to within a factor of 2 of that of the original Kepler mission. .
Figure 2.3 An example of the jump in flux of the correctedK2 photometry (red points) after the spacecraft is turned to point back to Earth for data upload (which occurs during the gap in the light curve). The black data points show the raw light curve prior to the Vanderburg reduction.
We made two modifications to the Vanderburg light curves in order to eliminate long- term variations in the signals (including instrumental effects that were still present after the Vanderburg reduction) and focus on the detection of short-period (P <4 days) modulations expected from stars with short rotation periods. Despite the removal of the effects of the spacecraft motion, “jumps” remained in the light curve, associated with when the telescope turns back to Earth to upload data, see Figure 2.3. In order to correct for this effect, we detrended the time series by fitting a third order polynomial to the data and subtracting this polynomial off the original time series. Next, we obtained a time series of the residuals
from the original K2 light curve using the following equation,
fi =
Fi−< F >
< F > , (2.1)
where fi is the residual flux for theith data point in normalized flux units,Fi is the raw flux value for theith data point, and< F > is the mean of the original time series, given by the equation,
< F >=
Pn
i=1Fi
n , (2.2)
where F is, again, the raw flux value and n is the length, i.e. number of data points, of the original time series. We then ran these detrended and mean-normalized light curves through our cross-correlation algorithm (which does not require uniform sampling - see below) to identify periodic or recurrent signals.