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Register a new userA uniform analysis of HD209458b Spitzer/IRAC lightcurves with Gaussian process models
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We present an analysis of Spitzer/IRAC primary transit and secondary eclipse lightcurves measured for HD209458b, using Gaussian process models to marginalise over the intrapixel sensitivity variations in the 3.6 micron and 4.5 micron channels and the ramp effect in the 5.8 micron and 8.0 micron channels. The main advantage of this approach is that we can account for a broad range of degeneracies between the planet signal and systematics without actually having to specify a deterministic functional form for the latter. Our results do not confirm a previous claim of water absorption in transmission. Instead, our results are more consistent with a featureless transmission spectrum, possibly due to a cloud deck obscuring molecular absorption bands. For the emission data, our values are not consistent with the thermal inversion in the dayside atmosphere that was originally inferred from these data. Instead, we agree with another re-analysis of these same data, which concluded a non-inverted atmosphere provides a better fit. We find that a solar-abundance clear-atmosphere model without a thermal inversion underpredicts the measured emission in the 4.5 micron channel, which may suggest the atmosphere is depleted in carbon monoxide. An acceptable fit to the emission data can be achieved by assuming that the planet radiates as an isothermal blackbody with a temperature of $1484pm 18$ K.
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The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid- and far-infrared, at wavelengths $>$3 $mu$m, have been taken with Spitzer. In some cases, in the past years, repeated observations and multiple reanalyses of the same datasets led to discrepant results, raising questions about the accuracy and reproducibility of such measurements. Morello et al. 2014, 2015 proposed a blind-source separation method based on the Independent Component Analysis of pixel time series (pixel-ICA) to analyze IRAC data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-S/N cases. We describe the method and discuss the results obtained over twelve eclipses of the exoplanet XO3b observed during the Warm Spitzer era in the 4.5 $mu$m band. The final results will be reported also in Ingalls et al. (in prep.), together with results obtained with other detrending methods, and over ten synthetic eclipses that were analyzed for the IRAC Data Challenge 2015. Our results are consistent within 1 $sigma$ with the ones reported in Wong et al. 2014. The self-consistency of individual measurements of eclipse depth and phase curve slope over a span of more than three years proves the stability of Warm Spitzer/IRAC photometry within the error bars, at the level of 1 part in 10$^4$ in stellar flux.
We present here a reanalysis of the Spitzer Space Telescope phase curves of the hot Jupiter WASP43 b, using the wavelet pixel-Independent Component Analysis, a blind signal-source separation method. The data analyzed were recorded with the InfraRed Array Camera and consisted of two visits at 3.6 $mu$m, and one visit at 4.5 $mu$m, each visit covering one transit and two eclipse events. To test the robustness of our technique we repeated the analysis on smaller portions of the phase curves, and by employing different instrument ramp models. Our reanalysis presents significant updates of the planetary parameters compared to those reported in the original phase curve study of WASP43 b. In particular, we found (1) higher nightside temperatures, (2) smaller hotspot offsets, (3) a greater consistency ($sim$1 $sigma$) between the two 3.6~$mu$m visits, and (4) a greater similarity with the predictions of the atmospheric circulation models. Our parameter results are consistent within 1 $sigma$ with those reported by a recent reanalysis of the same data sets. For each visit we studied the variation of the retrieved transit parameters as a function of various sets of stellar limb-darkening coefficients, finding significant degeneracy between the limb-darkening models and the analysis output. Furthermore, we performed the analysis of the single transit and eclipse events, and we examined the differences between these results with the ones obtained with the whole phase curve. Finally we provide a formula useful to optimize the trade-off between precision and duration of observations of transiting exoplanets.
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.
We use statistical equilibrium equations to investigate the IRAC color space of shocked molecular hydrogen. The location of shocked H_2 in [3.6]-[4.5] vs [4.5]-[5.8] color is determined by the gas temperature and density of neutral atomic hydrogen. We find that high excitation H_2 emission falls in a unique location in the color-color diagram and can unambiguously be distinguished from stellar sources. In addition to searching for outflows, we show that the IRAC data can be used to map the thermal structure of the shocked gas. We analyze archival Spitzer data of Herbig-Haro object HH 54 and create a temperature map, which is consistent with spectroscopically determined temperatures.
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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