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Register a new userTESS Data for Asteroseismology: Light Curve Systematics Correction
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Added by
Mikkel N{\\o}rup Lund
Publication date
2021
fields
Physics
and research's language is
English
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Data from the Transiting Exoplanet Survey Satellite (TESS) has produced of order one million light curves at cadences of 120 s and especially 1800 s for every ~27-day observing sector during its two-year nominal mission. These data constitute a treasure trove for the study of stellar variability and exoplanets. However, to fully utilize the data in such studies a proper removal of systematic noise sources must be performed before any analysis. The TESS Data for Asteroseismology (TDA) group is tasked with providing analysis-ready data for the TESS Asteroseismic Science Consortium, which covers the full spectrum of stellar variability types, including stellar oscillations and pulsations, spanning a wide range of variability timescales and amplitudes. We present here the two current implementations for co-trending of raw photometric light curves from TESS, which cover different regimes of variability to serve the entire seismic community. We find performance in terms of commonly used noise statistics to meet expectations and to be applicable to a wide range of different intrinsic variability types. Further, we find that the correction of light curves from a full sector of data can be completed well within a few days, meaning that when running in steady-state our routines are able to process one sector before data from the next arrives. Our pipeline is open-source and all processed data will be made available on TASOC and MAST.
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Over the last two decades, asteroseismology has increasingly proven to be the observational tool of choice for the study of stellar physics, aided by the high quality of data available from space-based missions such as CoRoT, Kepler, K2 and TESS. TESS in particular will produce more than an order of magnitude more such data than has ever been available before. While the standard TESS mission products include light curves from 120-sec observations suitable for both exoplanet and asteroseismic studies, they do not include light curves for the vastly larger number of targets observed by the mission at a longer 1800-sec cadence in Full Frame Images (FFIs). To address this lack, the TESS Data for Asteroseismology (TDA) group under the TESS Asteroseismic Science Consortium (TASC), has constructed an open-source pipeline focused on producing light curves for all stars observed by TESS at all cadences, currently including stars down to a TESS magnitude of 15. The pipeline includes target identification, background estimation and removal, correction of FFI timestamps, and a range of potential photometric extraction methodologies, though aperture photometry is currently the default approach. For the brightest targets, we transparently apply a halo photometry algorithm to construct a calibrated light curve from unsaturated pixels in the image. In this paper, we describe in detail the algorithms, functionality, and products of this pipeline, and summarize the noise metrics for the light curves. Companion papers will address the removal of systematic noise sources from our light curves, and a stellar variability classification from these.
We present ARC2 (Astrophysically Robust Correction 2), an open-source Python-based systematics-correction pipeline to correct for the Kepler prime mission long cadence light curves. The ARC2 pipeline identifies and corrects any isolated discontinuities in the light curves, then removes trends common to many light curves. These trends are modelled using the publicly available co-trending basis vectors, within an (approximate) Bayesian framework with `shrinkage priors to minimise the risk of over-fitting and the injection of any additional noise into the corrected light curves, while keeping any astrophysical signals intact. We show that the ARC2 pipelines performance matches that of the standard Kepler PDC-MAP data products using standard noise metrics, and demonstrate its ability to preserve astrophysical signals using injection tests with simulated stellar rotation and planetary transit signals. Although it is not identical, the ARC2 pipeline can thus be used as an open source alternative to PDC-MAP, whenever the ability to model the impact of the systematics removal process on other kinds of signal is important.
The Transiting Exoplanet Survey Satellite (TESS) is NASAs latest space telescope dedicated to the discovery of transiting exoplanets around nearby stars. Besides the main goal of the mission, asteroseismology is an important secondary goal and very relevant for the high-quality time series that TESS will make during its two year all-sky survey. Using TESS for asteroseismology introduces strong timing requirements, especially for coherent oscillators. Although the internal clock on board TESS is precise in its own time, it might have a constant drift and will thus need calibration, or offsets might inadvertently be introduced. Here we present simultaneously ground- and space-based observations of primary eclipses of several binary systems in the Southern ecliptic hemisphere, used to verify the reliability of the TESS timestamps. From twelve contemporaneous TESS/ground observations we determined a time offset equal to 5.8 +/- 2.5 sec, in the sense that the Barycentric time measured by TESS is ahead of real time. The offset is consistent with zero at 2.3-sigma level. In addition, we used 405 individually measured mid-eclipse times of 26 eclipsing binary stars observed solely by TESS to test the existence of a potential drift with a monotonic growth (or decay) affecting the observations of all stars. We find a drift corresponding to sigma_drift = 0.009 +/- 0.015 sec/day. We find that the measured offset is of a size that will not become an issue for comparing ground-based and space data for coherent oscillations for most of the targets observed with TESS.
We present K2SC (K2 Systematics Correction), a Python pipeline to model instrumental systematics and astrophysical variability in light curves from the K2 mission. K2SC uses Gaussian process regression to model position-dependent systematics and time-dependent variability simultaneously, enabling the user to remove both (e.g., for transit searches) or to remove systematics while preserving variability (for variability studies). For periodic variables, K2SC automatically computes estimates of the period, amplitude and evolution timescale of the variability. We apply K2SC to publicly available K2 data from campaigns 3--5, showing that we obtain photometric precision approaching that of the original Kepler mission. We compare our results to other publicly available K2 pipelines, showing that we obtain similar or better results, on average. We use transit injection and recovery tests to evaluate the impact of K2SC on planetary transit searches in K2 PDC (Pre-search Data Conditioning) data, for planet-to-star radius ratios down Rp/Rstar = 0.01 and periods up to P = 40 d, and show that K2SC significantly improves the ability to distinguish between correct and false detections, particularly for small planets. K2SC can be run automatically on many light curves, or manually tailored for specific objects such as pulsating stars or large amplitude eclipsing binaries. It can be run on ASCII and FITS light curve files, regardless of their origin. Both the code and the processed light curves are publicly available, and we provide instructions for downloading and using them. The methodology used by K2SC will be applicable to future transit search missions such as TESS and PLATO.
NASAs TESS mission citep{tess} has produced high precision photometry of millions of stars to the community. The majority of TESS observations have a duration of $approx$27 days, corresponding to a single observation during a TESS sector. A small subset of TESS targets are observed for multiple sectors, with approximately 1-2% of targets falling in the Continuous Viewing Zone (CVZ) during the prime mission citep{yield}, where targets are observed continuously for a year. These targets are highly valuable for extracting long period rotation rates, which can be linked to stellar ages. We present a pip installable Python tool for extracting long period rotation rates in the TESS CVZ, while simultaneously mitigating instrument systematics.
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