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Register a new userThe Kepler Smear Campaign: Light curves for 102 Very Bright Stars
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We present the first data release of the Kepler Smear Campaign, using collateral smear data obtained in the Kepler four-year mission to reconstruct light curves of 102 stars too bright to have been otherwise targeted. We describe the pipeline developed to extract and calibrate these light curves, and show that we attain photometric precision comparable to stars analyzed by the standard pipeline in the nominal Kepler mission. In this paper, aside from publishing the light curves of these stars, we focus on 66 red giants for which we detect solar-like oscillations, characterizing 33 of these in detail with spectroscopic chemical abundances and asteroseismic masses as benchmark stars. We also classify the whole sample, finding nearly all to be variable, with classical pulsations and binary effects. All source code, light curves, TRES spectra, and asteroseismic and stellar parameters are publicly available as a Kepler legacy sample.
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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.
The Kepler Mission has provided unprecedented, nearly continuous photometric data of $sim$200,000 objects in the $sim$105 deg$^{2}$ field of view from the beginning of science operations in May of 2009 until the loss of the second reaction wheel in May of 2013. The Kepler Eclipsing Binary Catalog contains information including but not limited to ephemerides, stellar parameters and analytical approximation fits for every known eclipsing binary system in the Kepler Field of View. Using Target Pixel level data collected from Kepler in conjunction with the Kepler Eclipsing Binary Catalog, we identify false positives among eclipsing binaries, i.e. targets that are not eclipsing binaries themselves, but are instead contaminated by eclipsing binary sources nearby on the sky and show eclipsing binary signatures in their light curves. We present methods for identifying these false positives and for extracting new light curves for the true source of the observed binary signal. For each source, we extract three separate light curves for each quarter of available data by optimizing the signal-to-noise ratio, the relative percent eclipse depth and the flux eclipse depth. We present 289 new eclipsing binaries in the Kepler Field of View that were not targets for observation, and these have been added to the Catalog. An online version of this Catalog with downloadable content and visualization tools is maintained at http://keplerEBs.villanova.edu.
In this note we present the starry_process code, which implements an interpretable Gaussian process (GP) for modeling variability in stellar light curves. As dark starspots rotate in and out of view, the total flux received from a distant star will change over time. Unresolved flux time series therefore encode information about the spatial structure of features on the stellar surface. The starry_process software package allows one to easily model the flux variability due to starspots, whether one is interested in understanding the properties of these spots or marginalizing over the stellar variability when it is treated as a nuisance signal. The main difference between the GP implemented here and typical GPs used to model stellar variability is the explicit dependence of our GP on physical properties of the star, such as its period, inclination, and limb darkening coefficients, and on properties of the spots, such as their radius and latitude distributions. This code is the Python implementation of the interpretable GP algorithm developed in Luger, Foreman-Mackey, and Hedges (2021).
The use of Gaussian processes (GPs) as models for astronomical time series datasets has recently become almost ubiquitous, given their ease of use and flexibility. GPs excel in particular at marginalization over the stellar signal in cases where the variability due to starspots rotating in and out of view is treated as a nuisance, such as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest since it is not obvious how the parameters of the GP model are related to the physical properties of interest, such as the size, contrast, and latitudinal distribution of the spots. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is degenerate, ill-posed, and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many light curves. In this paper, we derive a closed-form expression for the mean and covariance of a Gaussian process model that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more stellar light curves, including the typical radii and the mean and variance of the latitude distribution of starspots. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from both light curves and radial velocity (RV) measurements, as well as for robustly modeling correlated noise in both transiting and RV exoplanet searches. Our implementation is efficient, user-friendly, and open source, available as the Python package starry-process.
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