Analysis of Exoplanet Detection Methods using Machine Learning and Deep Neural Networks
International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023 - Proceedings, Page: 1242-1247
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Around 10 sextillion (1021) to a septillion (1024) stars have been estimated to exist in the observable universe. Only 5346 planets outside our solar system have been discovered. Humans are yet to find faster and more advanced techniques to find more earth-like planets. This also requires consistent enhancement of current telescopes and instruments. This study reviews various observational techniques like transits, radial velocities, and spectroscopic analysis. This also includes their associated algorithms like the box-fitting least square (BLS) algorithm, the transit least square (TLS) algorithm, the spectral deconvolution, and machine learning and deep learning techniques like neural networks and the ExoMiner algorithm. This provides a fundamental description of the necessity of these techniques, the fundamental concept behind them, the breakthroughs they have made, and the observational limitations of each method and their accuracies in exoplanet exploration.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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