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Detecting exoplanets with machine learning: A comparative study between convolutional neural networks and support vector machines
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this project two machine learning methods, Support Vector Machine, SVM, and Convolutional Neural Network, CNN, are studied to determine which method performs best on a labeled data set containing time series of light intensity from extrasolar stars.

The main difficulty is that in the data set there are a lot more non exoplanet stars than there are stars with orbiting exoplanets. This is causing a so called imbalanced data set which in this case is improved by i.e. mirroring the curves of stars with an orbiting exoplanet and adding them to the set. Trying to improve the results further, some preprocessing is done before implementing the methods on the data set. For the SVM, feature extraction and fourier transform of the time-series are important measures but further preprocessing alternatives are investigated. For the CNN-method the time-series are both detrended and smoothed, giving two inputs for the same light curve. All code is implemented in python.

Of all the validation parameters recall is considered the main priority since it is more important to find all exoplanets than finding all non exoplanets. CNN turned out to be the best performing method for the chosen configurations with 1.000 in recall which exceeds SVM’s recall 0.800. Considering the second validation parameter precision CNN is also the best performing method with a precision of 0.769 over SVM's 0.571.

Place, publisher, year, edition, pages
2019. , p. 35
Series
TVE-F ; 19021
Keywords [en]
Machine learning, Exoplanet, Support vector machine, Convolution neuralnetwork
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:uu:diva-385690OAI: oai:DiVA.org:uu-385690DiVA, id: diva2:1325376
External cooperation
Precisit AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-15 Last updated: 2019-06-24Bibliographically approved

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