| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/48AE23B | |||
| Repository | sid.inpe.br/mtc-m21d/2023/01.02.11.03 (restricted access) | |||
| Last Update | 2023:01.02.11.03.49 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2023/01.02.11.03.49 | |||
| Metadata Last Update | 2023:01.10.17.38.27 (UTC) administrator | |||
| DOI | 10.1007/s10569-022-10110-7 | |||
| ISSN | 0923-2958 | |||
| Citation Key | CarrubaAljCarDomMar:2022:OpArNe | |||
| Title | Optimization of artificial neural networks models applied to the identification of images of asteroids’ resonant arguments  | |||
| Year | 2022 | |||
| Month | Dec. | |||
| Access Date | 2024, May 14 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 3772 KiB | |||
| 2. Context | ||||
| Author | 1 Carruba, Valério 2 Aljbaae, Safwan 3 Caritá, Gabriel Antonio 4 Domingos, R. C. 5 Martins, B. | |||
| ORCID | 1 0000-0003-2786-0740 | |||
| Group | 1 2 DIMEC-CGCE-INPE-MCTI-GOV-BR 3 CMC-ETES-DIPGR-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade Estadual Paulista (UNESP) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Universidade Estadual Paulista (UNESP) 5 Universidade Estadual Paulista (UNESP) | |||
| Author e-Mail Address | 1 valerio.carruba@unesp.br 2 safwan.aljbaae@gmail.com 3 gabrielcarita@gmail.com | |||
| Journal | Celestial Mechanics and Dynamical Astronomy | |||
| Volume | 134 | |||
| Number | 6 | |||
| Pages | e59 | |||
| Secondary Mark | A2_ENGENHARIAS_III B1_INTERDISCIPLINAR B1_ASTRONOMIA_/_FÍSICA B2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B3_ENSINO B3_CIÊNCIA_DA_COMPUTAÇÃO | |||
| History (UTC) | 2023-01-02 11:03:49 :: simone -> administrator :: 2023-01-02 11:03:50 :: administrator -> simone :: 2022 2023-01-02 11:04:29 :: simone -> administrator :: 2022 2023-01-04 07:39:06 :: administrator -> simone :: 2022 2023-01-04 12:03:09 :: simone -> administrator :: 2022 2023-01-10 17:38:27 :: 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 | |||
| Keywords | Asteroids General Minor planets Time domain astronomy Time series analysis | |||
| Abstract | The asteroidal main belt is crossed by a web of mean motion and secular resonances that occur when there is a commensurability between fundamental frequencies of the asteroids and planets. Traditionally, these objects were identified by visual inspection of the time evolution of their resonant argument, which is a combination of orbital elements of the asteroid and the perturbing planet(s). Since the population of asteroids affected by these resonances is, in some cases, of the order of several thousand, this has become a taxing task for a human observer. Recent works used convolutional neural network (CNN) models to perform such task automatically. In this work, we compare the outcome of such models with those of some of the most advanced and publicly available CNN architectures, like the VGG, Inception, and ResNet. The performance of such models is first tested and optimized for overfitting issues, using validation sets and a series of regularization techniques like data augmentation, dropout, and batch normalization. The three best-performing models were then used to predict the labels of larger testing databases containing thousands of images. The VGG model, with and without regularizations, proved to be the most efficient method to predict labels of large datasets. Since the Vera C. Rubin observatory is likely to discover up to four million new asteroids in the next few years, the use of these models might become quite valuable to identify populations of resonant minor bodies. | |||
| Area | ETES | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCE > Optimization of artificial... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | s10569-022-10110-7.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher denyfinaldraft12 | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | urlib.net/www/2021/06.04.03.40.25 | |||
| Next Higher Units | 8JMKD3MGPCW/3F2UALS 8JMKD3MGPCW/46KTFK8 | |||
| Citing Item List | sid.inpe.br/bibdigital/2022/04.03.17.52 4 sid.inpe.br/bibdigital/2013/10.14.00.13 1 | |||
| Dissemination | WEBSCI; PORTALCAPES. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||