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Register a new userDetection of exomoons in simulated light curves with a regularized convolutional neural network
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Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets. We test the feasibility of a supervised convolutional neural network to classify photometric transit light curves of planet-host stars and identify exomoon transits, while avoiding false positives caused by stellar variability or instrumental noise. Convolutional neural networks are known to have contributed to improving the accuracy of classification tasks. The network optimization is typically performed without studying the effect of noise on the training process. Here we design and optimize a 1D convolutional neural network to classify photometric transit light curves. We regularize the network by the total variation loss in order to remove unwanted variations in the data features. Using numerical experiments, we demonstrate the benefits of our network, which produces results comparable to or better than the standard network solutions. Most importantly, our network clearly outperforms a classical method used in exoplanet science to identify moon-like signals. Thus the proposed network is a promising approach for analyzing real transit light curves in the future.
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The increasing number of transiting exoplanets sparked a significant interest in discovering their moons. Most of the methods in the literature utilize timing analysis of the raw light curves. Here we propose a new approach for the direct detection of a moon in the transit light curves via the so called Scatter Peak. The essence of the method is the valuation of the local scatter in the folded light curves of many transits. We test the ability of this method with different simulations: Kepler short cadence, Kepler long cadence, ground-based millimagnitude photometry with 3-min cadence, and the expected data quality of the planned ESA mission of PLATO. The method requires ~100 transit observations, therefore applicable for moons of 10-20 day period planets, assuming 3-4-5 year long observing campaigns with space observatories. The success rate for finding a 1 R_Earth moon around a 1 R_Jupiter exoplanet turned out to be quite promising even for the simulated ground-based observations, while the detection limit of the expected PLATO data is around 0.4 R_Earth. We give practical suggestions for observations and data reduction to improve the chance of such a detection: (i) transit observations must include out-of-transit phases before and after a transit, spanning at least the same duration as the transit itself; (ii) any trend filtering must be done in such a way that the preceding and following out-of-transit phases remain unaffected.
The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that is both computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.
We present a novel, iterative method using an empirical Bayesian approach for modeling the limb darkened WASP-121b transit from the TESS light curve. Our method is motivated by the need to improve $R_{p}/R_{ast}$ estimates for exoplanet atmosphere modeling, and is particularly effective with the limb darkening (LD) quadratic law requiring no prior central value from stellar atmospheric models. With the non-linear LD law, the method has all the advantages of not needing atmospheric models but does not converge. The iterative method gives a different $R_{p}/R_{ast}$ for WASP-121b at a significance level of 1$sigma$ when compared with existing non-iterative methods. To assess the origins and implications of this difference, we generate and analyze light curves with known values of the limb darkening coefficients (LDCs). We find that non-iterative modeling with LDC priors from stellar atmospheric models results in an inconsistent $R_{p}/R_{ast}$ at 1.5$sigma$ level when the known LDC values are as those previously found when modeling real data by the iterative method. In contrast, the LDC values from the iterative modeling yields the correct value of $R_{p}/R_{ast}$ to within 0.25$sigma$. For more general cases with different known inputs, Monte Carlo simulations show that the iterative method obtains unbiased LDCs and correct $R_{p}/R_{ast}$ to within a significance level of 0.3$sigma$. Biased LDC priors can cause biased LDC posteriors and lead to bias in the $R_{p}/R_{ast}$ of up to 0.82$%$, 2.5$sigma$ for the quadratic law and 0.32$%$, 1.0$sigma$ for the non-linear law. Our improvement in $R_{p}/R_{ast}$ estimation is important when analyzing exoplanet atmospheres.
We report first multicolor polarimetric measurements (UBV bands) for the hot Jupiters HD189733b and confirm our previously reported detection of polarization in the B band (Berdyugina et al. 2008). The wavelength dependence of polarization indicates the dominance of Rayleigh scattering with a peak in the blue B and U bands of ~10^-4+/-10^-5 and at least a factor of two lower signal in the V band. The Rayleigh-like wavelength dependence, detected also in the transmitted light during transits, implies a rapid decrease of the polarization signal toward longer wavelengths. Therefore, the nondetection by Wiktorowicz (2009), based on a measurement integrated within a broad passband covering the V band and partly B and R bands, is inconclusive and consistent with our detection in B. We discuss possible sources of the polarization and demonstrate that effects of incomplete cancellation of stellar limb polarization due to starspots or tidal perturbations are negligible as compared to scattering polarization in the planetary atmosphere. We compare the observations with a Rayleigh-Lambert model and determine effective radii and geometrical albedos for different wavelengths. We find a close similarity of the wavelength dependent geometrical albedo with that of the Neptune atmosphere, which is known to be strongly influenced by Rayleigh and Raman scattering. Our result establishes polarimetry as a reliable means for directly studying exoplanetary atmospheres.
The number of known transiting exoplanets is rapidly increasing, which has recently inspired significant interest as to whether they can host a detectable moon. Although there has been no such example where the presence of a satellite was proven, several methods have already been investigated for such a detection in the future. All these methods utilize post-processing of the measured light curves, and the presence of the moon is decided by the distribution of a timing parameter. Here we propose a method for the detection of the moon directly in the raw transit light curves. When the moon is in transit, it puts its own fingerprint on the intensity variation. In realistic cases, this distortion is too little to be detected in the individual light curves, and must be amplified. Averaging the folded light curve of several transits helps decrease the scatter, but it is not the best approach because it also reduces the signal. The relative position of the moon varies from transit to transit, the moons wing will appear in different positions on different sides of the planets transit. Here we show that a careful analysis of the scatter curve of the folded light curves enhances the chance of detecting the exomoons directly.
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