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Published March 10, 2022 | Published + Submitted
Journal Article Open

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 software butterpy used to synthesize the light curves using realistic starspot evolution.

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 April 28; revised 2022 January 7; accepted 2022 January 7; published 2022 March 17. We thank the anonymous reviewer for thoughtful and useful comments that improved the quality and results of this work. We also wish to acknowledge Gagandeep Anand, Connor Auge, Ashley Chontos, Aidan Chun, Curt Dodds, Ryan Dungee, Kyle Hart, Jason Hinkle, Rae Holcomb, Daniel Huber, Miles Lucas, Sushant Mahajan, Anna Payne, Nicholas Saunders, Jessica Schonhut-Stasik, Benjamin Shappee, Xudong Sun, and Jamie Tayar for fruitful conversations that improved the quality of this work. The technical support and advanced computing resources from the University of Hawaii Information Technology Services Cyberinfrastructure are gratefully acknowledged. This research was supported in part by the National Science Foundation under grant No. NSF PHY-1748958. J.v.S. and Z.R.C. acknowledge support from the National Aeronautics and Space Administration (grant Nos. 80NSSC21K0246, 80NSSC18K18584). J.L. acknowledges support from NASA through an Astrophysics Data Analysis Program grant to Lowell Observatory (grant No. 80NSSC20K1001). This paper includes data collected by the TESS mission. Funding for the TESS mission is provided by the NASA's Science Mission Directorate. Software: NumPy (Harris et al. 2020), Pandas (McKinney 2010; The pandas development team 2020), Matplotlib (Hunter 2007), AstroPy (Astropy Collaboration et al. 2013, 2018), SciPy (Virtanen et al. 2020), PyTorch (Paszke et al. 2019), Lightkurve (Lightkurve Collaboration et al. 2018), TESScut (Brasseur et al. 2019), iPython (Perez & Granger 2007), butterpy (Claytor et al. 2021), starspot (Angus 2021).

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Published - Claytor_2022_ApJ_927_219.pdf

Submitted - 2104.14566.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023