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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/44STT9H
Repositorysid.inpe.br/mtc-m21c/2021/06.18.17.58
Last Update2021:06.18.17.58.45 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m21c/2021/06.18.17.58.45
Metadata Last Update2022:04.03.19.24.46 (UTC) administrator
DOI10.1093/mnras/stab914
ISSN0035-8711
1365-2966
Citation KeyCarrubaAljbDomiBarl:2021:ArNeNe
TitleArtificial neural network classification of asteroids in the M1:2 mean-motion resonance with Mars
Year2021
MonthJune
Access Date2025, Aug. 09
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4793 KiB
2. Context
Author1 Carruba, Valério
2 Aljbaae, Safwan
3 Domingos, R. C.
4 Barletta, W.
ORCID1 0000-0003-2786-0740
Group1
2 DIMEC-CGCE-INPE-MCTI-GOV-BR
Affiliation1 Universidade Estadual Paulista (UNESP)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade Estadual Paulista (UNESP)
4 Universidade Estadual Paulista (UNESP)
Author e-Mail Address1 valerio.carruba@unesp.br
2 safwan.aljbaae@gmail.com
JournalMonthly Notices of the Royal Astronomical Society
Volume504
Number1
Pages692-700
Secondary MarkA1_QUÍMICA A1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_III A2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA A2_ASTRONOMIA_/_FÍSICA B2_ENSINO B5_ENGENHARIAS_IV
History (UTC)2021-06-18 17:58:45 :: simone -> administrator ::
2022-04-03 19:24:46 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsmethods: data analysis
celestial mechanics
minor planets
asteroids: general
AbstractArtificial neural networks (ANNs) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work, we used for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars. Our model was able to perform well above 85 per cent levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision, and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.
AreaETES
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCE > Artificial neural network...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34R/44STT9H
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/44STT9H
Languageen
Target Filecarruba_artificial.pdf
User Groupsimone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KTFK8
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.17.52 - 13
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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