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Register a new userProperties of a Previously Unidentified Instrumental Signature in $textit{Kepler/K2}$ that was Confused for AGN Variability
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The $textit{Kepler}$ satellite potentially provides the highest precision photometry of active galactic nuclei (AGN) available to investigate short-timescale optical variability. We targeted quasars from the Sloan Digital Sky Survey that lie in the fields of view of the $textit{Kepler/K2}$ campaigns. Based on those observations, we report the discovery and properties of a previously unidentified instrumental signature in K2. Systematic errors in K2, beyond those due to the motion of the detector, plague our AGN and other faint-target, guest-observer science proposals. Weakly illuminated pixels are dominated by low frequency trends that are both non-astrophysical and correlated from object to object. A critical clue to understanding this instrumental noise is that different targets observed in the same channels of Campaign 8 (rear facing) and Campaign 16 (forward facing) had nearly identical light curves after time reversal of one of the campaigns. This observation strongly suggests that the underlying problem relates to the relative Sun-spacecraft-field orientation, which was approximately the same on day 1 of Campaign 8 as the last day of Campaign 16. Furthermore, we measure that the instrumental signature lags in time as a function of radius from the center of the detector, crossing channel boundaries. Systematics documented in this investigation are unlikely to be due to Moir{e} noise, rolling band, or pointing jitter. Instead this work strongly suggests temperature-dependent focus changes that are further subject to channel variations. Further characterization of this signature is crucial for rehabilitating K2 data for use in investigations of AGN light curves.
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The advent of new time domain surveys and the imminent increase in astronomical data expose the shortcomings in traditional time series analysis (such as power spectra analysis) in characterising the abundantly varied, complex and stochastic light curves of Active Galactic Nuclei (AGN). Recent applications of novel methods from non-linear dynamics have shown promise in characterising higher modes of variability and time-scales in AGN. Recurrence analysis in particular can provide complementary information about characteristic time-scales revealed by other methods, as well as probe the nature of the underlying physics in these objects. Recurrence analysis was developed to study the recurrences of dynamical trajectories in phase space, which can be constructed from one-dimensional time series such as light curves. We apply the methods of recurrence analysis to two optical light curves of Kepler-monitored AGN. We confirm the detection and period of an optical quasi-periodic oscillation in one AGN, and confirm multiple other time-scales recovered from other methods ranging from 5 days to 60 days in both objects. We detect regions in the light curves that deviate from regularity, provide evidence of determinism and non-linearity in the mechanisms underlying one light curve (KIC 9650712), and determine a linear stochastic process recovers the dominant variability in the other light curve (Zwicky 229--015). We discuss possible underlying processes driving the dynamics of the light curves and their diverse classes of variability.
We present a numerical framework for the variability of active galactic nuclei (AGN), which links the variability of AGN over a broad range of timescales and luminosities to the observed properties of the AGN population as a whole, and particularly the Eddington ratio distribution function (ERDF). We have implemented our framework on GPU architecture, relying on previously published time series generating algorithms. After extensive tests that characterise several intrinsic and numerical aspects of the simulations, we describe some applications used for current and future time domain surveys and for the study of extremely variable sources (e.g., changing look or flaring AGN). Specifically, we define a simulation setup which reproduces the AGN variability observed in the PTF/iPTF survey, and use it to forward model longer light curves of the kind that may be observed within the LSST main survey. Thanks to our effcient implementations, these simulations are able to cover for example over 1 Myr with a roughly weekly cadence. We envision that this framework will become highly valuable to prepare for, and best exploit, data from upcoming time domain surveys, such as for example LSST.
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.
We report rotation periods, variability characteristics, gyrochronological ages for ~950 of the Kepler Object of Interest host stars. We find a wide dispersion in the amplitude of the photometric variability as a function of rotation, likely indicating differences in the spot distribution among stars. We use these rotation periods in combination with published spectroscopic measurements of vsini and stellar parameters to derive the stellar inclination in the line-of-sight, and find a number of systems with possible spin-orbit misalignment. We additionally find several systems with close-in planet candidates whose stellar rotation periods are equal to or twice the planetary orbital period, indicative of possible tidal interactions between these planets and their parent stars. If these systems survive validation to become confirmed planets, they will provide important clues to the evolutionary history of these systems.
We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximise the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self Organising Maps (SOM, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0-4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 Delta Scuti pulsators, 133 Gamma Doradus pulsators, 183 detached eclipsing binaries, 290 semi-detached or contact eclipsing binaries and 9399 other periodic (mostly spot-modulated) sources, once class significance cuts are taken into account. We present lightcurve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in Python as part of the open source PyMVPA package, which in combination with already available RF modules can be easily used to recreate the method.
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