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Register a new userPrecise time-series photometry for the Kepler-2.0 mission
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The recently approved NASA K2 mission has the potential to multiply by an order of magnitude the number of short-period transiting planets found by Kepler around bright and low-mass stars, and to revolutionise our understanding of stellar variability in open clusters. However, the data processing is made more challenging by the reduced pointing accuracy of the satellite, which has only two functioning reaction wheels. We present a new method to extract precise light curves from K2 data, combining list-driven, soft-edged aperture photometry with a star-by-star correction of systematic effects associated with the drift in the roll-angle of the satellite about its boresight. The systematics are modelled simultaneously with the stars intrinsic variability using a semi-parametric Gaussian process model. We test this method on a week of data collected during an engineering test in January 2014, perform checks to verify that our method does not alter intrinsic variability signals, and compute the precision as a function of magnitude on long-cadence (30-min) and planetary transit (2.5-hour) timescales. In both cases, we reach photometric precisions close to the precision reached during the nominal Kepler mission for stars fainter than 12th magnitude, and between 40 and 80 parts per million for brighter stars. These results confirm the bright prospects for planet detection and characterisation, asteroseismology and stellar variability studies with K2. Finally, we perform a basic transit search on the light curves, detecting 2 bona fide transit-like events, 7 detached eclipsing binaries and 13 classical variables.
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In astronomy, we are witnessing an enormous increase in the number of source detections, precision, and diversity of measurements. Additionally, multi-epoch data is becoming the norm, making time-series analyses an important aspect of current astronomy. The Gaia mission is an outstanding example of a multi-epoch survey that provides measurements in a large diversity of domains, with its broad-band photometry; spectrophotometry in blue and red (used to derive astrophysical parameters); spectroscopy (employed to infer radial velocities, v sin(i), and other astrophysical parameters); and its extremely precise astrometry. Most of all that information is provided for sources covering the entire sky. Here, we present several properties related to the Gaia time series, such as the time sampling; the different types of measurements; the Gaia G, G BP and G RP-band photometry; and Gaia-inspired studies using the CORrelation-RAdial-VELocities data to assess the potential of the information on the radial velocity, the FWHM, and the contrast of the cross-correlation function. We also present techniques (which are used or are under development) that optimize the extraction of astrophysical information from the different instruments of Gaia, such as the principal component analysis and the multi-response regression. The detailed understanding of the behavior of the observed phenomena in the various measurement domains can lead to richer and more precise characterization of the Gaia data, including the definition of more informative attributes that serve as input to (our) machine-learning algorithms.
PLATO 2.0 has recently been selected for ESAs M3 launch opportunity (2022/24). Providing accurate key planet parameters (radius, mass, density and age) in statistical numbers, it addresses fundamental questions such as: How do planetary systems form and evolve? Are there other systems with planets like ours, including potentially habitable planets? The PLATO 2.0 instrument consists of 34 small aperture telescopes (32 with 25 sec readout cadence and 2 with 2.5 sec candence) providing a wide field-of-view (2232 deg2) and a large photometric magnitude range (4-16 mag). It focusses on bright (4-11 mag) stars in wide fields to detect and characterize planets down to Earth-size by photometric transits, whose masses can then be determined by ground-based radial-velocity follow-up measurements. Asteroseismology will be performed for these bright stars to obtain highly accurate stellar parameters, including masses and ages. The combination of bright targets and asteroseismology results in high accuracy for the bulk planet parameters: 2%, 4-10% and 10% for planet radii, masses and ages, respectively. The planned baseline observing strategy includes two long pointings (2-3 years) to detect and bulk characterize planets reaching into the habitable zone (HZ) of solar-like stars and an additional step-and-stare phase to cover in total about 50% of the sky. PLATO 2.0 will observe up to 1,000,000 stars and detect and characterize hundreds of small planets, and thousands of planets in the Neptune to gas giant regime out to the HZ. It will therefore provide the first large-scale catalogue of bulk characterized planets with accurate radii, masses, mean densities and ages. This catalogue will include terrestrial planets at intermediate orbital distances, where surface temperatures are moderate. Coverage of this parameter range with statistical numbers of bulk characterized planets is unique to PLATO 2.0.
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.
We develop a new photometry algorithm that is optimized for $Spitzer$ time series in crowded fields and that is particularly adapted to faint and/or heavily blended targets. We apply this to the 170 targets from the 2015 $Spitzer$ microlensing campaign and present the results of three variants of this algorithm in an online catalog. We present detailed accounts of the application of this algorithm to two difficult cases, one very faint and the other very crowded. Several of $Spitzer$s instrumental characteristics that drive the specific features of this algorithm are shared by $Kepler$ and $WFIRST$, implying that these features may prove to be a useful starting point for algorithms designed for microlensing campaigns by these other missions.
We present high cadence (1-10 hr^-1) time-series photometry of the eruptive young variable star V1647 Orionis during its 2003-2004 and 2008-2009 outbursts. The 2003 light curve was obtained mid-outburst at the phase of steepest luminosity increase of the system, during which time the accretion rate of the system was presumably continuing to increase toward its maximum rate. The 2009 light curve was obtained after the system luminosity had plateaued, presumably when the rate of accretion had also plateaued. We detect a flicker noise signature in the power spectrum of the lightcurves, which may suggest that the stellar magnetosphere continued to interact with the accretion disk during each outburst event. Only the 2003 power spectrum, however, evinces a significant signal with a period of 0.13 d. While the 0.13 d period cannot be attributed to the stellar rotation period, we show that it may plausibly be due to short-lived radial oscillations of the star, possibly caused by the surge in the accretion rate.
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