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تسجيل مستخدم جديدPushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning
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Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections.
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Direct imaging allows for the detection and characterization of exoplanets via their thermal emission. We report the discovery via imaging of a young Jovian planet in a triple star system and characterize its atmospheric properties through near-infra
red spectroscopy. The semi-major axis of the planet is closer relative to that of its hierarchical triple star system than for any known exoplanet within a stellar binary or triple, making HD 131399 dynamically unlike any other known system. The location of HD 131399Ab on a wide orbit in a triple system demonstrates that massive planets may be found on long and possibly unstable orbits in multi-star systems. HD 131399Ab is one of the lowest mass (4+/-1 MJup) and coldest (850+/-50 K) exoplanets to have been directly imaged.
Direct imaging is a powerful exoplanet discovery technique that is complementary to other techniques and offers great promise in the era of 30 meter class telescopes. Space-based transit surveys have revolutionized our understanding of the frequency
of planets at small orbital radii around Sun-like stars. The next generation of extremely large ground-based telescopes will have the angular resolution and sensitivity to directly image planets with $R < 4R_oplus$ around the very nearest stars. Here, we predict yields from a direct imaging survey of a volume-limited sample of Sun-like stars with the Mid-Infrared ELT Imager and Spectrograph (METIS) instrument, planned for the 39 m European Southern Observatory (ESO) Extremely Large Telescope (ELT) that is expected to be operational towards the end of the decade. Using Kepler occurrence rates, a sample of stars with spectral types A-K within 6.5 pc, and simulated contrast curves based on an advanced model of what is achievable from coronagraphic imaging with adaptive optics, we estimated the expected yield from METIS using Monte Carlo simulations. We find the METIS expected yield of planets in the N2 band (10.10 - 12.40 $mu$m) is 1.14 planets, which is greater than comparable observations in the L (3.70 - 3.95 $mu$m) and M (4.70 - 4.90 $mu$m) bands. We also determined a 24.6% chance of detecting at least one Jovian planet in the background limited regime assuming a 1 hour integration. We calculated the yield per star and estimate optimal observing revisit times to increase the yield. We also analyzed a northern hemisphere version of this survey and found there are additional targets worth considering. In conclusion, we present an observing strategy aimed to maximize the possible yield for limited telescope time, resulting in 1.48 expected planets in the N2 band.
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Ground-based telescopes coupled with adaptive optics (AO) have been playing a leading role in exoplanet direct imaging science and technological development for the past two decades and will continue to have an indispensable role for the next decade
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