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Astraea: Predicting Long Rotation Periods with 27-Day Light Curves | | Yuxi
; Ruth Angus
; Marcel A.~Agüeros
; Kirsten Blancato
; Melissa Ness
; Jason L.~Curtis
; Sam Grunblatt
; | | Date: |
7 Aug 2020 | | Abstract: | The rotation periods of planet-hosting stars can be used for modeling and
mitigating the impact of magnetic activity in radial velocity measurements and
can help constrain the high-energy flux environment and space weather of
planetary systems. Millions of stars and thousands of planet hosts are observed
with the Transiting Exoplanet Survey Satellite (TESS). However, most will only
be observed for 27 contiguous days in a year, making it difficult to measure
rotation periods with traditional methods. This is especially problematic for
field M dwarfs, which are ideal candidates for exoplanet searches, but which
tend to have periods in excess of the 27-day observing baseline. We present a
new tool, Astraea, for predicting long rotation periods from short-duration
light curves combined with stellar parameters from Gaia DR2. Using Astraea, we
can predict the rotation periods from Kepler 4-year light curves with 13%
uncertainty overall (and a 9% uncertainty for periods > 30 days). By training
on 27-day Kepler light curve segments, Astraea can predict rotation periods up
to 150 days with 9% uncertainty (5% for periods > 30 days). After training this
tool on these 27-day Kepler light curve segments, we applied exttt{Astraea}
to real TESS data. For the 195 stars that were observed by both Kepler and
TESS, we were able to predict the rotation periods with 55% uncertainty despite
the wild differences in systematics. | | Source: | arXiv, 2008.03351 | | Services: | Forum | Review | PDF | Favorites | |
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