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تسجيل مستخدم جديدExoplanet Detection Techniques
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We are still in the early days of exoplanet discovery. Astronomers are beginning to model the atmospheres and interiors of exoplanets and have developed a deeper understanding of processes of planet formation and evolution. However, we have yet to map out the full complexity of multi-planet architectures or to detect Earth analogues around nearby stars. Reaching these ambitious goals will require further improvements in instrumentation and new analysis tools. In this chapter, we provide an overview of five observational techniques that are currently employed in the detection of exoplanets: optical and IR Doppler measurements, transit photometry, direct imaging, microlensing, and astrometry. We provide a basic description of how each of these techniques works and discuss forefront developments that will result in new discoveries. We also highlight the observational limitations and synergies of each method and their connections to future space missions.
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We provide a revised assessment of the number of exoplanets that should be discovered by Gaia astrometry, extending previous studies to a broader range of spectral types, distances, and magnitudes. Our assessment is based on a large representative sa
mple of host stars from the TRILEGAL Galaxy population synthesis model, recent estimates of the exoplanet frequency distributions as a function of stellar type, and detailed simulation of the Gaia observations using the updated instrument performance and scanning law. We use two approaches to estimate detectable planetary systems: one based on the S/N of the astrometric signature per field crossing, easily reproducible and allowing comparisons with previous estimates, and a new and more robust metric based on orbit fitting to the simulated satellite data. With some plausible assumptions on planet occurrences, we find that some 21,000 (+/-6000) high-mass (1-15M_J) long-period planets should be discovered out to distances of ~500pc for the nominal 5-yr mission (including at least 1000-1500 around M dwarfs out to 100pc), rising to some 70,000 (+/-20,000) for a 10-yr mission. We indicate some of the expected features of this exoplanet population, amongst them ~25-50 intermediate-period (P~2-3yr) transiting systems.
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of thes
e methods to improve the conventional algorithm based approaches presently used in astrophysics to detect exoplanets. Using the time-series analysis library TSFresh to analyse light curves, we extracted 789 features from each curve, which capture the information about the characteristics of a light curve. We then used these features to train a gradient boosting classifier using the machine learning tool lightgbm. This approach was tested on simulated data, which showed that is more effective than the conventional box least squares fitting (BLS) method. We further found that our method produced comparable results to existing state-of-the-art deep learning models, while being much more computationally efficient and without needing folded and secondary views of the light curves. For Kepler data, the method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent of the true planet signals are ranked higher than non-planet signals. The resulting recall is 0.96, so that 96 per cent of real planets are classified as planets. For the Transiting Exoplanet Survey Satellite (TESS) data, we found our method can classify light curves with an accuracy of 0.98, and is able to identify planets with a recall of 0.82 at a precision of 0.63.
A machine learning technique with two-dimension convolutional neural network is proposed for detecting exoplanet transits. To test this new method, five different types of deep learning models with or without folding are constructed and studied. The
light curves of the Kepler Data Release 25 are employed as the input of these models. The accuracy, reliability, and completeness are determined and their performances are compared. These results indicate that a combination of two-dimension convolutional neural network with folding would be an excellent choice for the future transit analysis.
In contrast to photometric transits, whose peak signal occurs at mid-transit due to occultation of the brightest region of the disk, polarimetric transits provide a signal upon ingress and egress due to occultation of the polarized stellar limb. Limb
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We report the detection of an atmosphere on a rocky exoplanet, GJ 1132 b, which is similar to Earth in terms of size and density. The atmospheric transmission spectrum was detected using Hubble WFC3 measurements and shows spectral signatures of aeros
ol scattering, HCN, and CH$_{4}$ in a low mean molecular weight atmosphere. We model the atmospheric loss process and conclude that GJ 1132 b likely lost the original H/He envelope, suggesting that the atmosphere that we detect has been reestablished. We explore the possibility of H$_{2}$ mantle degassing, previously identified as a possibility for this planet by theoretical studies, and find that outgassing from ultrareduced magma could produce the observed atmosphere. In this way we use the observed exoplanet transmission spectrum to gain insights into magma composition for a terrestrial planet. The detection of an atmosphere on this rocky planet raises the possibility that the numerous powerfully irradiated Super-Earth planets, believed to be the evaporated cores of Sub-Neptunes, may, under favorable circumstances, host detectable atmospheres.
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