From 2009-2013, the Kepler space telescope monitored over 150,000 stars in its search for extrasolar planets. Kepler used the transiting method, watching its targets stars almost continuously for four years, searching for signs of a possible planet as it transits in front of its star, blocking some of the star's light and therefore making the star appear to dim temporarily at regular intervals. As of this writing, Kepler's data has been used to find over 3,600 planet candidates, of which 961 have been classified as definite planets, while the rest comprise a mixture of real planets and false positives with statuses yet to be determined.
With the transiting method, it is easier to find planets that are large – like Jupiter in our solar system – and close to their star – closer in many cases than Mercury is to the Sun. Accordingly, Kepler found many planets that are larger than Earth, and many that are closer to their stars than Earth, and are therefore hot. Kepler also found some planets Earth-sized or smaller and some other planets about the same temperature as Earth, but it is not clear yet if it detected any planets that were earthlike in terms of both size and temperature, which would seemingly make them good candidates for supporting earthlike habitats and perhaps life. Unfortunately, that combination of properties also makes earthlike planets hard for Kepler to detect, even if they are in fact common. This is because small planet with earthlike temperature would create transit signals that are relatively weak and, during Kepler's four years in service, relatively few in number. To search for planets like these depends upon an excellent understanding of the noise, or random variation (not literally sound like we hear), in Kepler's measurements of a star's brightness.
The following points are particularly relevant:
1) Kepler discovered that stars are noisier than was expected. This is a scientific discovery in its own right, but unfortunately, it means that finding earthlike planets is more difficult than expected.
2) Kepler’s instrument exhibited significant discrepancies in noise levels across the surface of its detector.
Making the situation more complicated, mission operations required Kepler to rotate 90° clockwise in one maneuver at the end of each three-month quarter, so the noisy areas of its electronics moved from one place to another in Kepler's observed sky field, returning to the same place every four quarters. In addition, the level of electronic noise depended upon the temperature of the electronics, and so the extent of the problem varied from quarter to quarter and even within quarters. Analyzing Kepler observations without taking into account these seasonal noise variations leads to the detection of electronic false positives, anomalies where purely coincidental fluctuations in a star's brightness are misinterpreted as the transits of a real planet.
What makes this particularly insidious is that false positives caused by Kepler's noisy electronics often fit this profile: Three relatively minor dimming events that occur four quarters apart as the same noisy region on Kepler's detector surface goes through four 90° rotations to come back around to report that a star experienced three dimming events with a period of about a year. That is very much like what a real earthlike planet orbiting a sunlike star would look like in the data. Therefore, any real earthlike planets in Kepler's data are lost in a much larger number of false positives.
This problem showed up profoundly in a December 2012 release of Kepler results. That analysis used the first three years of observations to produce a list of 18,406 possible planets. These entities, called Threshold Crossing Events (TCEs) are results at an early stage in the data processing, with further checks needed to validate which of the TCEs might be planets and which are false positives.
If we define “earthlike” as planets that have radius 50–125% of the Earth’s and receive radiative heating from their star at levels between that of Venus and Mars, then the TCE release contained 87 possible earthlike planets. This news produced a great deal of excitement because it would mean that at least a few earthlike planets had been found among those 87 even if only a few percent of them were real. However, because of the noise problem, it is credible that each of the 87 was an electronic false positive (caused by noise) or astrophysical false positive (the detection of a real astronomical object that only seems to be an earthlike planet).
A dramatic way to illustrate the problem of false positives caused by electronic noise is to look only at those TCEs which were detected as a result of three total dimming events occurring four quarters apart. This means that the same region of detector surface was used to detect all three dimming events. Because the time between dimming events was about a year, we call these Annual TCEs. They are displayed in Figure 1, below (click to enlarge).
Figure 1: Annual TCE: TCEs detected as three possible transits at four-quarter intervals in the same location of Kepler’s detector surface. 1a: Annual TCEs plotted according to sky coordinates, color-coded by season. 1b: Annual TCEs plotted according to instrument surface coordinates, color-coded by number of Annual TCEs per star, with black=1, blue=2, red≥3. Shaded boxes indicate selected regions of elevated incidence of Annual TCEs.
Figure 1a shows the Annual TCEs as they appear in the sky with the number for the corresponding instrument module during Spring observation seasons (Quarters 1, 5, 9, and 13). In each successive quarter, Kepler rotated 90° clockwise around the center of this field so that, for example, Module 2 spent Summer quarters observing the same patch of sky that Module 10 watched during Spring quarters. The most obvious motif in Figure 1a is a collection of three prominent square regions seen in sectors 7, 9, and 17, which are densely packed with TCEs as a result of the same anomalous electronics hosted in Module 17 rotating throughout the seasons, creating a high number of false positives in all of the seasons except Winter. This anomaly is seen more prominently in Figure 1b, which plots the Annual TCEs in instrument coordinates, so all four seasons' TCEs associated with the same area of the electronic detector surface appear atop one another. This shows the unique nature of the Module 17 anomaly; although there are other areas of anomalously many Annual TCEs, none is as large and densely packed as this one. Modules 9 and 18 contain two of the next-most prominent anomalous regions. Overall, the anomalies are most prevalent in the upper right and lower left quadrants of this display.
It may seem tempting, at this point, to try to create a purified data set by identifying all combinations of detector area and season that produce an undue number of false positives, and excluding them from further consideration. In the ideal case, we might hope to identify a sufficient number of anomalies to exclude, keep the rest of the observations, and thus arrive at a set of TCEs relatively cleansed of electronic false positives.
Achieving his goal is not as straightforward as a casual glance might seem to indicate. While the areas shaded in pink certainly contain many of the total false positives, that does not imply that the areas outside them lack false positives. Note the number of blue and red points plotted in Figure 1b. Each of these is a Kepler object where multiple TCEs with a period of about one year were detected. In many of these cases, the data, if all such TCEs were planets, would show that the star hosted multiple planets with about the same orbital period, and in many cases implausibly close to one another at the time of detection. These orbits are not plausible, so most blue points and essentially all red ones indicate electronic false positives. These systems are concentrated in the most anomalous zones, but are scattered all around the detector surface, indicating that the problem is not confined to a limited set of anomalous zones. To produce a clean set of possible planets, we need to account for the specific conditions that cause electronic false positives.
The basic problem described above has led to Geoffrey Marcy and Erik Petigura of UC Berkeley to create their own data processing pipeline, called TERRA, which accounts for the same systematic sources of noise I described above. In my next post, I’ll provide my description of the noise and how it can be accounted for in order to focus on which of those 87 earthlike TCEs might be real planets.