NASA's Kepler Mission, which employs transit photometry to discover new worlds, has been the single largest contributor to the overall census of known planets. The spacecraft's CCD detectors are sensitive enough that it is feasible to detect some short-period planets through the variation in their intrinsic emission and reflected starlight during the course of their orbits. A search for these traces of planetary flux was part of the early renditions of the Kepler analysis pipeline, and was called the Reflected Light Search, or RLS module. Budget constraints, however, forced cancellation of the reflected light search as the mission moved toward launch.
In a new paper lead-authored by Millholland, machine-learning techniques are applied to identify non-transiting hot Jupiter-type planets in the archival Kepler data. The pipeline described in the new study has identified sixty high-quality giant planet candidates, all of which are now in need of confirmation via the Doppler velocity technique.
The feasibility of obtaining definitive radial velocity observations for the candidates is quite high. Although many of the target stars are faint, the planets, should they exist, are expected to have large radial velocity semi-amplitudes. The confirmation of a significant number of the candidate planets would provide a highly useful ensemble for studies of how planetary atmospheres and climates react to extreme conditions of stellar insolation.
In cases where prospective host stars receive five or more Doppler velocity observations of mean estimated instrumental velocity precision of 15 m/s or better, will 50% or more of the candidates listed in Table 4 of the paper turn out to be bona-fide hot Jupiters?
For purposes of question resolution, a successful detection must be published in the peer-reviewed literature prior to July 1, 2018, and must be consistent with a planet having 0.2 Mjup < Msin(i) < 13 Mjup, and a period within 10% of the value listed in Table 4 of the paper.