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Register a new userAutomated preparation of Kepler time series of planet hosts for asteroseismic analysis
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One of the tasks of the Kepler Asteroseismic Science Operations Center (KASOC) is to provide asteroseismic analyses on Kepler Objects of Interest (KOIs). However, asteroseismic analysis of planetary host stars presents some unique complications with respect to data preprocessing, compared to pure asteroseismic targets. If not accounted for, the presence of planetary transits in the photometric time series often greatly complicates or even hinders these asteroseismic analyses. This drives the need for specialised methods of preprocessing data to make them suitable for asteroseismic analysis. In this paper we present the KASOC Filter, which is used to automatically prepare data from the Kepler/K2 mission for asteroseismic analyses of solar-like planet host stars. The methods are very effective at removing unwanted signals of both instrumental and planetary origins and produce significantly cleaner photometric time series than the original data. The methods are automated and can therefore easily be applied to a large number of stars. The application of the filter is not restricted to planetary hosts, but can be applied to any solar-like or red giant stars observed by Kepler/K2.
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The Kepler mission is providing photometric data of exquisite quality for the asteroseismic study of different classes of pulsating stars. These analyses place particular demands on the pre-processing of the data, over a range of timescales from minutes to months. Here, we describe processing procedures developed by the Kepler Asteroseismic Science Consortium (KASC) to prepare light curves that are optimized for the asteroseismic study of solar-like oscillating stars in which outliers, jumps and drifts are corrected.
The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large datasets. Gaussian Processes are a popular class of models used for this purpose but, since the computational cost scales, in general, as the cube of the number of data points, their application has been limited to small datasets. In this paper, we present a novel method for Gaussian Process modeling in one-dimension where the computational requirements scale linearly with the size of the dataset. We demonstrate the method by applying it to simulated and real astronomical time series datasets. These demonstrations are examples of probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra, and transiting planet parameters. The method exploits structure in the problem when the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise. This form of covariance arises naturally when the process is a mixture of stochastically-driven damped harmonic oscillators -- providing a physical motivation for and interpretation of this choice -- but we also demonstrate that it can be a useful effective model in some other cases. We present a mathematical description of the method and compare it to existing scalable Gaussian Process methods. The method is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. We provide well-tested and documented open-source implementations of this method in C++, Python, and Julia.
We report here on our search for excess power in photometry of Neptune collected by the K2 mission that may be due to intrinsic global oscillations of the planet Neptune. To conduct this search, we developed new methods to correct for instrumental effects such as intrapixel variability and gain variations. We then extracted and analyzed the time-series photometry of Neptune from 49 days of nearly continuous broadband photometry of the planet. We find no evidence of global oscillations and place an upper limit of $sim$5 ppm at 1000 uhz for the detection of a coherent signal. With an observed cadence of 1-minute and point-to-point scatter less than 0.01%, the photometric signal is dominated by reflected light from the Sun, which is in turn modulated by atmospheric variability of Neptune at the 2% level. A change in flux is also observed due to the increasing distance between Neptune and the K2 spacecraft, and solar variability with convection-driven solar p modes present.
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.
High-precision time series photometry with the Kepler satellite has been crucial to our understanding both of exoplanets, and via asteroseismology, of stellar physics. After the failure of two reaction wheels, the Kepler satellite has been repurposed as Kepler-2 (K2), observing fields close to the ecliptic plane. As these fields contain many more bright stars than the original Kepler field, K2 provides an unprecedented opportunity to study nearby objects amenable to detailed follow-up with ground-based instruments. Due to bandwidth constraints, only a small fraction of pixels can be downloaded, with the result that most bright stars which saturate the detector are not observed. We show that engineering data acquired for photometric calibration, consisting of collateral `smear measurements, can be used to reconstruct light curves for bright targets not otherwise observable with Kepler/K2. Here we present some examples from Kepler Quarter 6 and K2 Campaign 3, including the delta Scuti variables HD 178875 and 70 Aqr, and the red giant HR 8500 displaying solar-like oscillations. We compare aperture and smear photometry where possible, and also study targets not previously observed. These encouraging results suggest this new method can be applied to most Kepler and K2 fields.
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