| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/4877PCP | |||
| Repository | sid.inpe.br/mtc-m21d/2022/12.13.14.14 | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/12.13.14.14.26 | |||
| Metadata Last Update | 2023:01.03.16.46.26 (UTC) administrator | |||
| Secondary Key | INPE--PRE/ | |||
| Citation Key | CarrubaAljbDomiMart:2022:ArNeNe | |||
| Title | Artificial neural network classification of asteroids in the M1:2 meanmotion resonance with Mars  | |||
| Year | 2022 | |||
| Access Date | 2024, May 14 | |||
| Secondary Type | PRE CN | |||
| 2. Context | ||||
| Author | 1 Carruba, V. 2 Aljbaae, Safwan 3 Domingos, R. C. 4 Martins, Bruno | |||
| Group | 1 2 DIMEC-CGCE-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade Estadual Paulista (UNESP) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade Estadual Paulista (UNESP) 4 Universidade Estadual Paulista (UNESP) | |||
| Author e-Mail Address | 1 2 safwan.aljbaae@gmail.com 3 4 bruno.s.martins@unesp.br | |||
| Conference Name | Colóquio Brasileiro de Dinâmica Orbital, 221 | |||
| Conference Location | 12-16 dez. 2022 | |||
| Date | São José dos Campos, SP | |||
| History (UTC) | 2022-12-13 14:14:26 :: simone -> administrator :: 2023-01-03 16:46:26 :: administrator -> simone :: 2022 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Abstract | Artificial 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. | |||
| Area | ETES | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCE > Artificial neural network... | |||
| doc Directory Content | there are no files | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
| |||
| 4. Conditions of access and use | ||||
| Language | en | |||
| User Group | simone | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/46KTFK8 | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label lineage mark mirrorrepository nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle size sponsor subject targetfile tertiarymark tertiarytype type url volume | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||