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Register a new userRevisiting Spitzer transit observations with Independent Component Analysis: new results for the GJ436 system
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We analyzed four Spitzer/IRAC observations at 3.6 and 4.5 {mu}m of the primary transit of the exoplanet GJ436b, by using blind source separation techniques. These observations are important to investigate the atmospheric composition of the planet GJ436b. Previous analyses claimed strong inter-epoch variations of the transit parameters due to stellar variability, casting doubts on the possibility to extract conclusively an atmospheric signal; those analyses also reported discrepant results, hence the necessity of this reanalysis. The method we used has been proposed in Morello et al. (2014) to analyze 3.6 {mu}m transit light-curves of the hot Jupiter HD189733b; it performes an Independent Component Analysis (ICA) on a set of pixel-light-curves, i.e. time series read by individual pixels, from the same photometric observation. Our method only assumes the independence of instrumental and astrophysical signals, and therefore guarantees a higher degree of objectivity compared to parametric detrending techniques published in the literature. The datasets we analyzed in this paper represent a more challenging test compared to the previous ones. Contrary to previous results reported in the literature, our results (1) do not support any detectable inter-epoch variations of orbital and stellar parameters, (2) are photometrically stable at the level 10e-4 in the IR, and (3) the transit depth measurements at the two wavelengths are consistent within 1{sigma}. We also (4) detect a possible transit duration variation (TDV) of 80 s (2 {sigma} significance level), that has not been pointed out in the literature, and (5) confirm no transit timing variations (TTVs) >30 s.
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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.
Observations of the Kepler-1625 system with the Kepler and Hubble Space Telescopes have suggested the presence of a candidate exomoon, Kepler-1625b I, a Neptune-radius satellite orbiting a long-period Jovian planet. Here we present a new analysis of the Hubble observations, using an independent data reduction pipeline. We find that the transit light curve is well fit with a planet-only model, with a best-fit $chi^2_ u$ equal to 1.01. The addition of a moon does not significantly improve the fit quality. We compare our results directly with the original light curve from Teachey & Kipping (2018), and find that we obtain a better fit to the data using a model with fewer free parameters (no moon). We discuss possible sources for the discrepancy in our results, and conclude that the lunar transit signal found by Teachey & Kipping (2018) was likely an artifact of the data reduction. This finding highlights the need to develop independent pipelines to confirm results that push the limits of measurement precision.
We present a model-independent technique for calculating the time of mid-transits. This technique, named barycenter method, uses the light-curves symmetry to determine the transit timing by calculating the transit light-curve barycenter. Unlike the other methods of calculating mid-transit timing, this technique does not depend on the parameters of the system and central star. We demonstrate the capabilities of the barycenter method by applying this technique to some known transiting systems including several emph{Kepler} confirmed planets. Results indicate that for complete and symmetric transit lightcurves, the barycenter method achieves the same precision as other techniques, but with fewer assumptions and much faster. Among the transiting systems studied with the barycenter method, we focus in particular on LHS 6343C, a brown dwarf that transits a member of an M+M binary system, LHS 6343AB. We present the results of our analysis, which can be used to set an upper limit on the period and mass of a possible second small perturber.
Let $X$ be a mean zero Gaussian random vector in a separable Hilbert space ${mathbb H}$ with covariance operator $Sigma:={mathbb E}(Xotimes X).$ Let $Sigma=sum_{rgeq 1}mu_r P_r$ be the spectral decomposition of $Sigma$ with distinct eigenvalues $mu_1>mu_2> dots$ and the corresponding spectral projectors $P_1, P_2, dots.$ Given a sample $X_1,dots, X_n$ of size $n$ of i.i.d. copies of $X,$ the sample covariance operator is defined as $hat Sigma_n := n^{-1}sum_{j=1}^n X_jotimes X_j.$ The main goal of principal component analysis is to estimate spectral projectors $P_1, P_2, dots$ by their empirical counterparts $hat P_1, hat P_2, dots$ properly defined in terms of spectral decomposition of the sample covariance operator $hat Sigma_n.$ The aim of this paper is to study asymptotic distributions of important statistics related to this problem, in particular, of statistic $|hat P_r-P_r|_2^2,$ where $|cdot|_2^2$ is the squared Hilbert--Schmidt norm. This is done in a high-complexity asymptotic framework in which the so called effective rank ${bf r}(Sigma):=frac{{rm tr}(Sigma)}{|Sigma|_{infty}}$ (${rm tr}(cdot)$ being the trace and $|cdot|_{infty}$ being the operator norm) of the true covariance $Sigma$ is becoming large simultaneously with the sample size $n,$ but ${bf r}(Sigma)=o(n)$ as $ntoinfty.$ In this setting, we prove that, in the case of one-dimensional spectral projector $P_r,$ the properly centered and normalized statistic $|hat P_r-P_r|_2^2$ with {it data-dependent} centering and normalization converges in distribution to a Cauchy type limit. The proofs of this and other related results rely on perturbation analysis and Gaussian concentration.
Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply the FastICA to the component separation problem of the microwave background including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically we prepare 100GHz, 143GHz, and 217GHz mock microwave sky maps including galactic thermal dust, NANTEN CO line, and the Cosmic Microwave Background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that the FastICA can successfully estimate the CO component as the first independent component in our deflection algorithm as its distribution has the largest degree of non-Gaussianity among the components. By subtracting the CO and the dust components from the original sky maps, we will be able to make an unbiased estimate of the cosmological CMB angular power spectrum.
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