| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34R/3UME9F2 | |||
| Repository | sid.inpe.br/mtc-m21c/2020/01.03.15.45 (restricted access) | |||
| Last Update | 2020:01.03.15.45.43 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21c/2020/01.03.15.45.43 | |||
| Metadata Last Update | 2022:01.04.01.34.55 (UTC) administrator | |||
| DOI | 10.1016/j.ascom.2019.100334 | |||
| ISSN | 2213-1337 | |||
| Citation Key | BarchiCRSSMCGSM:2020:CoSt | |||
| Title | Machine and deep learning applied to galaxy morphology: a comparative study  | |||
| Year | 2020 | |||
| Month | Jan. | |||
| Access Date | 2024, May 17 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 5069 KiB | |||
| 2. Context | ||||
| Author | 1 Barchi, Paulo Henrique 2 Carvalho, Reinaldo Ramos de 3 Rosa, Reinaldo Roberto 4 Sautter, Rubens Andreas 5 Soares Santos, M. 6 Marques, B. A. D. 7 Clua, E. 8 Gonçalves, T. S. 9 Sá Freitas, C. de 10 Moura, T. C. | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JJ5B 3 8JMKD3MGP5W/3C9JJ5D | |||
| Group | 1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR 2 LABAC-COCTE-INPE-MCTIC-GOV-BR 3 LABAC-COCTE-INPE-MCTIC-GOV-BR 4 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Brandeis University 6 Universidade Federal Fluminense (UFF) 7 Universidade Federal Fluminense (UFF) 8 Universidade Federal do Rio de Janeiro (UFRJ) 9 Universidade Federal do Rio de Janeiro (UFRJ) 10 Universidade de São Paulo (USP) | |||
| Author e-Mail Address | 1 paulo.barchi@inpe.br 2 3 reinaldo.rosa@inpe.br 4 rubens.sautter@inpe.br | |||
| Journal | Astronomy and Computing | |||
| Volume | 30 | |||
| Pages | e100334 | |||
| History (UTC) | 2020-01-03 15:47:06 :: simone :: 2019 -> 2020 2020-01-03 15:47:06 :: simone -> administrator :: 2020 2022-01-04 01:34:55 :: administrator -> simone :: 2020 | |||
| 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 | Galaxies: photometry Methods: data analysis Machine learning Techniques: image processing Galaxies: General Catalogs | |||
| Abstract | Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification. | |||
| Area | COMP | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Machine and deep... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Machine and deep... | |||
| 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 | barchi_machine.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/3ESGTTP 8JMKD3MGPCW/3F2PHGS | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/09.22.23.14 4 sid.inpe.br/bibdigital/2013/10.12.22.16 2 sid.inpe.br/mtc-m21/2012/07.13.14.58.48 1 | |||
| Dissemination | SCOPUS | |||
| Host Collection | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||