| 000 | 02621naa a2200277 a 4500 | ||
|---|---|---|---|
| 003 | AR-LpUFIB | ||
| 005 | 20250311170511.0 | ||
| 008 | 230201s2020 xx o 000 0 eng d | ||
| 024 | 8 |
_aDIF-M8338 _b8558 _zDIF007630 |
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| 040 |
_aAR-LpUFIB _bspa _cAR-LpUFIB |
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| 100 | 1 | _aAidelman, Yael | |
| 245 | 1 | 0 | _aReddening-free Q indices to identify Be star candidates |
| 300 | _a1 archivo (851,7 kB) | ||
| 500 | _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aAstronomical databases currently provide high-volume spectroscopic and photometric data. While spectroscopic data is better suited to the analysis of many astronomical objects, photometric data is relatively easier to obtain due to shorter telescope usage time. Therefore, there is a growing need to use photometric information to automatically identify objects for further detailed studies, specially H emission line stars such as Be stars. Photometric color-color diagrams (CCDs) are commonly used to identify this kind of objects. However, their identification in CCDs is further complicated by the reddening effect caused by both the circumstellar and interstellar gas. This effect prevents the generalization of candidate identification systems. Therefore, in this work we evaluate the use of neural networks to identify Be star candidates from a set of OB-type stars. The networks are trained using a labeled subset of the VPHAS+ and 2MASS databases, with filters u, g, r, H, and K. In order to avoid the reddening effect, we propose and evaluate the use of reddening-free Q indices to enhance the generalization of the model to other databases and objects. To test the validity of the approach, we manually labeled a subset of the database, and use it to evaluate candidate identification models. We also labeled an independent dataset for cross dataset evaluation. We evaluate the recall of the models at a 99% precision level on both test sets. Our results show that the proposed features provide a significant improvement over the original filter magnitudes. | ||
| 534 | _aCloud Computing, Big Data & Emerging Topics, Conference, JCC-BD&ET, (8va : 2020 : La Plata, Argentina). Proceedings. Suiza, Springer Cham, 2020, pp. 111-123 | ||
| 650 | 4 | _aASTRONOMÍA | |
| 653 | _aestrellas tipo OB | ||
| 653 | _aclasificación estelar | ||
| 700 | 1 | _aEscudero, Carlos | |
| 700 | 1 | _aRonchetti, Franco | |
| 700 | 1 | _aQuiroga, Facundo Manuel | |
| 700 | 1 | _aLanzarini, Laura Cristina | |
| 856 | 4 | 0 | _uhttps://doi.org/10.48550/arXiv.2009.06017 |
| 942 | _cCP | ||
| 999 |
_c57403 _d57403 |
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