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Register a new userSpitzer IRAC Photometry for Time Series in Crowded Fields
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Added by
Sebastiano Calchi Novati
Publication date
2015
fields
Physics
and research's language is
English
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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.
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We present IRAC 3.6, 4.5, 5.8 and 8 micron photometry for the 17 A, K and M type members of the Eta Chameleontis association. These data show infrared excesses toward six of the 15 K and M stars, indicating the presence of circumstellar disks around 40% of the stars with masses of 0.1-1 solar mass. The two A-stars show no infrared excesses. The excess emission around one of the stars is comparable to the median excess for classical T Tauri stars in the Taurus association; the remaining five show comparatively weak excess emission. Taking into account published Halpha spectroscopy that shows that five of the six stars are accreting, we argue that the disks with weak mid-infrared excesses are disks in which the inner disks have been largely depleted of small grains by grain growth, or, in one case, the small grains have settled to the midplane. This suggests that Eta Cha has a much higher fraction of disks caught in the act of transitioning into optically thin disks than that measured in younger clusters and associations.
We present a new method employing machine learning techniques for measuring astrophysical features by correcting systematics in IRAC high precision photometry using Random Forests. The main systematic in IRAC light curve data is position changes due to unavoidable telescope motions coupled with an intrapixel response function. We aim to use the large amount of publicly available calibration data for the single pixel used for this type of work (the sweet spot pixel) to make a fast, easy to use, accurate correction to science data. This correction on calibration data has the advantage of using an independent dataset instead of using the science data on itself, which has the disadvantage of including astrophysical variations. After focusing on feature engineering and hyperparameter optimization, we show that a boosted random forest model can reduce the data such that we measure the median of ten archival eclipse observations of XO-3b to be 1459 +- 200 parts per million. This is a comparable depth to the average of those in the literature done by seven different methods, however the spread in measurements is 30-100% larger than those literature values, depending on the reduction method. We also caution others attempting similar methods to check their results with the fiducial dataset of XO-3b as we were also able to find models providing initially great scores on their internal test datasets but whose results significantly underestimated the eclipse depth of that planet.
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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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