Presearch Data Conditioning Research Articles

The highest mass Kepler red giants – I. Global asteroseismic parameters of 48 stars

ABSTRACT When low- and intermediate-mass stars evolve off the main sequence, they expand and cool into the red giant stages of evolution, which include those associated with shell H burning (the red giant branch), core He burning (the red clump), and shell He burning (the asymptotic giant branch). The majority of red giants have masses <2 M⊙, and red giants more massive than this are often excluded from major studies. Here, we present a study of the highest mass stars (M > 3.0 M⊙) in the Kepler sample of 16 000 red giants. We begin by re-estimating their global seismic properties with new light curves, highlighting the differences between using the simple aperture photometry and presearch data conditioning of simple aperture photometry light curves provided by Kepler. We use the re-estimated properties to derive new mass estimates for the stars, ending with a final sample of 48 confirmed high-mass stars. We explore their oscillation envelopes, confirming the trends found in recent works such as low mean mode amplitude and wide envelopes. We find, through probabilistic means, that our sample is likely all core He burning stars. We measure their dipole and quadrupole mode visibilities and confirm that the dipole mode visibility tends to decrease with mass.

The TESS–Keck Survey. I. A Warm Sub-Saturn-mass Planet and a Caution about Stray Light in TESS Cameras*

Abstract We report the detection of a Saturn-size exoplanet orbiting HD 332231 (TOI 1456) in light curves from the Transiting Exoplanet Survey Satellite (TESS). HD 332231—an F8 dwarf star with a V-band magnitude of 8.56—was observed by TESS in Sectors 14 and 15. We detect a single-transit event in the Sector 15 presearch data conditioning (PDC) light curve. We obtain spectroscopic follow-up observations of HD 332231 with the Automated Planet Finder, Keck I, and SONG telescopes. The orbital period we infer from radial velocity (RV) observations leads to the discovery of another transit in Sector 14 that was masked by PDC due to scattered light contamination. A joint analysis of the transit and RV data confirms the planetary nature of HD 332231 b, a Saturn-size ( ), sub-Saturn-mass ( ) exoplanet on a 18.71 day circular orbit. The low surface gravity of HD 332231 b and the relatively low stellar flux it receives make it a compelling target for transmission spectroscopy. Also, the stellar obliquity is likely measurable via the Rossiter–McLaughlin effect, an exciting prospect given the 0.14 au orbital separation of HD 332231 b. The spectroscopic observations do not provide substantial evidence for any additional planets in the HD 332231 system, but continued RV monitoring is needed to further characterize this system. We also predict that the frequency and duration of masked data in the PDC light curves for TESS Sectors 14–16 could hide transits of some exoplanets with orbital periods between 10.5 and 17.5 days.

Long rotation period main-sequence stars from Kepler SAP light curves

ABSTRACT Stellar rotation plays a key role in stellar activity. The rotation period could be detected through light curve variations caused by star-spots. Kepler provides two types of light curves: one is the Pre-search Data Conditioning (PDC) light curves, and the other is the Simple Aperture Photometer (SAP) light curves. Compared with the PDC light curves, the SAP light curves keep the long-term trend, relatively suitable for searches of long-period signals. However, SAP data are inflicted by some artefacts such as quarterly rolls and instrumental errors, making it difficult to find the physical periods in the SAP light curves. We explore a systematic approach based on the light curve pre-processing, period detection, and candidate selection. We also develop a simulated light curve test to estimate our detection limits for the SAP-like LCs. After applying our method to the raw SAP light curves, we found more than 1000 main-sequence stars with periods longer than 30 d; 165 are newly discovered. Considering the potential flaw of the SAP, we also inspect the newly found objects with photometry methods, and most of our periodical signals are confirmed.

KEPLER TRANSIT DEPTHS CONTAMINATED BY A PHANTOM STAR

ABSTRACT We present ground-based observations from the Discovery Channel Telescope (DCT) of three transits of Kepler-445c—a supposed super-Earth exoplanet with properties resembling GJ 1214b—and demonstrate that the transit depth is ∼50% shallower than the depth previously inferred from Kepler spacecraft data. The resulting decrease in planetary radius significantly alters the interpretation of the exoplanet’s bulk composition. Despite the faintness of the M4 dwarf host star, our ground-based photometry clearly recovers each transit and achieves repeatable 1σ precision of ∼0.2% (2 millimags). The transit parameters estimated from the DCT data are discrepant with those inferred from the Kepler data to at least 17σ confidence. This inconsistency is due to a subtle miscalculation of the stellar crowding metric during the Kepler pre-search data conditioning (PDC). The crowding metric, or CROWDSAP, is contaminated by a non-existent phantom star originating in the USNO-B1 catalog and inherited by the Kepler Input Catalog (KIC). Phantom stars in the KIC are likely rare, but they have the potential to affect statistical studies of Kepler targets that use the PDC transit depths for a large number of exoplanets where an individual follow-up observation of each is not possible. The miscalculation of Kepler-445c’s transit depth emphasizes the importance of stellar crowding in the Kepler data, and provides a cautionary tale for the analysis of data from the Transiting Exoplanet Survey Satellite, which will have even larger pixels than Kepler.

K2SC: Flexible systematics correction and detrending ofK2light curves using Gaussian Process regression

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.

Period analysis of two non-Blazhko RRab stars, FN Lyr and V894 Cyg, based on Kepler photometry: evidence of low-mass companions on wider orbits

Long-cadence-corrected pre-search data conditioning (PDC) fluxes of FN Lyr (KIC 6936115) and V894 Cyg (KIC 9591503), observed continuously by the Kepler mission, spanning over 1470 d, are used to determine hundreds of times of maximum and minimum for the analysis of O - C residuals. The interpretation of the clear variations in the O - C diagrams is that these are caused by the light-travel-time effect as a result of additional companions. The mass functions of the companions are f (M) = (3.94 +/- 0.82) x 10(-6) and (2.01 +/- 0.22) x 10(-4) M-circle dot. Assuming that the orbital plane inclination follows a random distribution, the companions to both stars can be constrained to be substellar objects (brown dwarf or giant planet), with 89.4 and 59.4 per cent probability, respectively. Under the assumption that the orbital inclination equals 90 degrees, the distances between the companions and the central RR Lyrae stars at periastron should be 1.03 and 0.50 au, respectively. In addition, the orbital periods are 794.8 and 1084.4 d for FN Lyr and V894 Cyg, respectively. By comparing these orbital parameters with those of B subdwarf stars in binary systems, there are strong hints that horizontal branch stars might have different evolution histories. The long-term pulsation period changes are also discussed. Based on our studies, RR Lyrae stars in binary systems are not rare, at least among binary systems with wider separations.

Astrophysically robust systematics removal using variational inference: application to the first month of Kepler data

Space-based transit search missions such as Kepler are collecting large numbers of stellar light curves of unprecedented photometric precision and time coverage. However, before this scientific goldmine can be exploited fully, the data must be cleaned of instrumental artefacts. We present a new method to correct common-mode systematics in large ensembles of very high precision light curves. It is based on a Bayesian linear basis model and uses shrinkage priors for robustness, variational inference for speed, and a de-noising step based on empirical mode decomposition to prevent the introduction of spurious noise into the corrected light curves. After demonstrating the performance of our method on a synthetic dataset, we apply it to the first month of Kepler data. We compare the results, which are publicly available, to the output of the Kepler pipeline's pre-search data conditioning, and show that the two generally give similar results, but the light curves corrected using our approach have lower scatter, on average, on both long and short timescales. We finish by discussing some limitations of our method and outlining some avenues for further development. The trend-corrected data produced by our approach are publicly available.

TRANSIT TIMING OBSERVATIONS FROM KEPLER . VIII. CATALOG OF TRANSIT TIMING MEASUREMENTS OF THE FIRST TWELVE QUARTERS

Following Ford et al. (2011, 2012) and Steffen et al. (2012) we derived the transit timing of 1960 Kepler KOIs using the pre-search data conditioning (PDC) light curves of the first twelve quarters of the Kepler data. For 721 KOIs with large enough SNRs, we obtained also the duration and depth of each transit. The results are presented as a catalog for the community to use. We derived a few statistics of our results that could be used to indicate significant variations. Including systems found by previous works, we have found 130 KOIs that showed highly significant TTVs, and 13 that had short-period TTV modulations with small amplitudes. We consider two effects that could cause apparent periodic TTV - the finite sampling of the observations and the interference with the stellar activity, stellar spots in particular. We briefly discuss some statistical aspects of our detected TTVs. We show that the TTV period is correlated with the orbital period of the planet and with the TTV amplitude.

Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

ABSTRACTWith the unprecedented photometric precision of the Kepler spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends, and other instrumental signatures, obscure astrophysical signals. The presearch data conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods. Variable stars are often of particular astrophysical interest, so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian maximum a posteriori (MAP) approach, where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set, which is in turn used to establish a range of “reasonable” robust fit parameters. These robust fit parameters are then used to generate a Bayesian prior and a Bayesian posterior probability distribution function (PDF) which, when maximized, finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection that commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian prior PDFs are generated from fits to the light-curve distributions themselves.

Kepler Presearch Data Conditioning I—Architecture and Algorithms for Error Correction in Kepler Light Curves

ABSTRACTKepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side effect of the removal of errors. In this article we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC version 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.