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Abstract The rotation period of a star is an important quantity that provides insight into its structure and state. For stars with surface features like starspots, their periods can be inferred from brightness variations as these features move across the stellar surface. TESS, with its all-sky coverage, is providing the largest sample of stars for obtaining rotation periods. However, most of the periods have been limited to shorter than the 13.7 days TESS orbital period due to strong background signals (e.g., scattered light) on those timescales. In this study, we investigated the viability of measuring longer periods (>10 days) from TESS light curves for stars in the Northern Continuous Viewing Zone (NCVZ). We first created a reference set of 272 period measurements longer than 10 days for K and M dwarfs in the NCVZ using data from the Zwicky Transient Facility (ZTF) that we consider as the “ground truth” given ZTF’s long temporal baseline of 6+ years. We then used theunpopularpipeline to detrend TESS light curves and implemented a modified Lomb–Scargle (LS) periodogram that accounts for flux offsets between observing sectors. For 179 out of the 272 sources (66%), the TESS-derived periods match the ZTF-derived periods to within 10%. The match rate increases to 81% (137 out of 170) when restricting to sources with a TESS LS power that exceeds a threshold. Our results confirm the capability of measuring periods longer than 10 days from TESS data, highlighting the data set’s potential for studying slow rotators.
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Recovery of TESS Stellar Rotation Periods Using Deep Learning
Abstract We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot-evolution patterns. We convolved these simulated light curves with real TESS light curves containing minimal intrinsic astrophysical variability to allow the network to learn TESS systematics and estimate rotation periods despite them. In addition to periods, we predict uncertainties via heteroskedastic regression to estimate the credibility of the period predictions. In the most credible half of the test data, we recover 10% accurate periods for 46% of the targets, and 20% accurate periods for 69% of the targets. Using our trained network, we successfully recover periods of real stars with literature rotation measurements, even past the 13.7 day limit generally encountered by TESS rotation searches using conventional period-finding techniques. Our method also demonstrates resistance to half-period aliases. We present the neural network and simulated training data, and introduce the softwarebutterpyused to synthesize the light curves using realistic starspot evolution.
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- Award ID(s):
- 1920304
- PAR ID:
- 10364044
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 927
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 219
- Size(s):
- Article No. 219
- Sponsoring Org:
- National Science Foundation
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