| 1. |
- Roettenbacher, Rachael M., et al.
(author)
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The Connection between Starspots and Flares on Main-sequence Kepler Stars
- 2018
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In: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 868:1
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Journal article (peer-reviewed)abstract
- Starspots and flares are indicators of stellar magnetic activity and can both be studied in greater detail by utilizing the long-term, space-based archive of the Kepler satellite. Here, we aim to investigate a subset of the Kepler archive to reveal a connection between the starspots and the stellar flares, in order to provide insight into the overall stellar magnetic field. We use the flare-finding algorithm FLATW'RM in conjunction with a new suite of algorithms that aim to locate the local minima caused by starspot groups. We compare the phase difference between the time of maximum flux of a flare and the time of minimum stellar flux due to a starspot group. The strongest flares do not appear to be correlated to the largest starspot group present, but are also not uniformly distributed in phase with respect to the starspot group. The weaker flares, however, do show an increased occurrence close to the starspot groups.
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| 2. |
- Vida, Krisztián, et al.
(author)
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Finding flares in Kepler data using machine-learning tools
- 2018
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 616
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Journal article (peer-reviewed)abstract
- Context. Archives of long photometric surveys, such as the Kepler database, are a great basis for studying flares. However, identifying the flares is a complex task; it is easily done in the case of single-target observations by visual inspection, but is nearly impossible for several year-long time series for several thousand targets. Although automated methods for this task exist, several problems are difficult (or impossible) to overcome with traditional fitting and analysis approaches.Aims. We introduce a code for identifying and analyzing flares based on machine-learning methods, which are intrinsically adept at handling such data sets.Methods. We used the RANSAC (RANdom SAmple Consensus) algorithm to model light curves, as it yields robust fits even in the case of several outliers, such as flares. The light curves were divided into search windows, approximately on the order of the stellar rotation period. This search window was shifted over the data set, and a voting system was used to keep false positives to a minimum: only those flare candidate points were kept that were identified as a flare in several windows.Results. The code was tested on short-cadence K2 observations of TRAPPIST-1 and on long-cadence Kepler data of KIC 1722506. The detected flare events and flare energies are consistent with earlier results from manual inspections.
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