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dc.contributor.authorSmullen, Rachel A
dc.contributor.authorVolk, Kathryn
dc.date.accessioned2021-04-15T00:53:37Z
dc.date.available2021-04-15T00:53:37Z
dc.date.issued2020-07-06
dc.identifier.citationSmullen, R. A., & Volk, K. (2020). Machine learning classification of Kuiper belt populations. Monthly Notices of the Royal Astronomical Society, 497(2), 1391-1403.en_US
dc.identifier.issn0035-8711
dc.identifier.doi10.1093/mnras/staa1935
dc.identifier.urihttp://hdl.handle.net/10150/657746
dc.description.abstractIn the outer Solar system, the Kuiper belt contains dynamical subpopulations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration. The subdivision of observed Kuiper belt objects (KBOs) into different dynamical classes is based on their current orbital evolution in numerical integrations of their orbits. Here, we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification. Using a Gradient Boosting Classifier, a type of machine learning regression tree classifier trained on features derived from short numerical simulations, we sort observed KBOs into four broad, dynamically distinct populations - classical, resonant, detached, and scattering - with a >97 per cent accuracy for the testing set of 542 securely classified KBOs. Over 80 per cent of these objects have a >3 sigma probability of class membership, indicating that the machine learning method is classifying based on the fundamental dynamical features of each population. We also demonstrate how, by using computational savings over traditional methods, we can quickly derive a distribution of class membership by examining an ensemble of object clones drawn from the observational errors. We find two major reasons for misclassification: inherent ambiguity in the orbit of the object - for instance, an object that is on the edge of resonance - and a lack of representative examples in the training set. This work provides a promising avenue to explore for fast and accurate classification of the thousands of new KBOs expected to be found by surveys in the coming decade.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.language.isoenen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.rights© 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectmethods: data analysisen_US
dc.subjectmethods: statisticalen_US
dc.subjectKuiper belt: generalen_US
dc.subjectplanets and satellites: dynamical evolution and stabilityen_US
dc.titleMachine learning classification of Kuiper belt populationsen_US
dc.typeArticleen_US
dc.identifier.eissn1365-2966
dc.contributor.departmentUniv Arizona, Dept Astronen_US
dc.contributor.departmentUniv Arizona, Lunar & Planetary Laben_US
dc.identifier.journalMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETYen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleMonthly Notices of the Royal Astronomical Society
dc.source.volume497
dc.source.issue2
dc.source.beginpage1391
dc.source.endpage1403
refterms.dateFOA2021-04-15T00:53:38Z


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