Multiband image classification of astronomical objects
Resumo
Astronomy has entered the era of large digital sky surveys, transitioning from a relatively data-scarce field of study to a very data-rich one. The images coming from these new surveys are hyperspectral (having up to a few dozen bands) and noisy (due to limitations on telescope resolution and atmospheric conditions), present faint and saturated signals, and can amount to tens of terabytes. This unique set of characteristics make them very attractive for trying out deep learning methods. In this short paper, we present a multiband image classifier for stars, galaxies and quasars, and propose steps towards a semi-supervised scheme that could enable the discovery of new objects.
Palavras-chave:
astronomical images, multispectral images, deep learning, semi-supervised learning
Referências
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” 2012. [Online]. http://doi.org/10.1145/3065386
B. Sesar et al, “Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample,” 2017. https://doi.org/10.3847/1538-3881/aa661b
C. Lintott et al, “Galaxy Zoo 1: Data Release of Morphological Classifications for nearly 900,000 galaxies,” January 2011. https://doi.org/10.1111/j.1365-2966.2010.17432.x
C. M. de Oliveira et al, “The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies and redshifts with 12 optical filters,” 2019. https://doi.org/10.1093/mnras/stz1985
D. S. Aguado et al, “The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA Derived Quantities, Data Visualization Tools and Stellar Library,” 2018. https://doi.org/10.3847/1538-4365/aaf651
J. A. Tyson and the LSST Collaboration, “Large Synoptic Survey Telescope: Overview,” 2003. https://doi.org/10.1117/12.456772
M. Duarte, L. M. Sampedro, and A. Molino, “The S-PLUS: the star/galaxy classification based on a Machine Learning approach,” 2018. https://arxiv.org/pdf/1805.02427.pdf
R. E. González, R. P. Muñoz, and C. A. Hernández, “Galaxy detection and identification using deep learning and data augmentation,” 2018. https://doi.org/10.1016/j.ascom.2018.09.004
R. Lupton et al, “Preparing Red-Green-Blue (RGB) Images from CCD Data,” 2003. https://doi.org/10.1086/382245
S. Dieleman, K. W. Willett, and J. Dambre, “Rotation-invariant convolutional neural networks for galaxy morphology prediction,” 2015. https://doi.org/10.1093/mnras/stv632
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” 2017. https://doi.org/10.1109/CVPR.2017.634
B. Sesar et al, “Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample,” 2017. https://doi.org/10.3847/1538-3881/aa661b
C. Lintott et al, “Galaxy Zoo 1: Data Release of Morphological Classifications for nearly 900,000 galaxies,” January 2011. https://doi.org/10.1111/j.1365-2966.2010.17432.x
C. M. de Oliveira et al, “The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies and redshifts with 12 optical filters,” 2019. https://doi.org/10.1093/mnras/stz1985
D. S. Aguado et al, “The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA Derived Quantities, Data Visualization Tools and Stellar Library,” 2018. https://doi.org/10.3847/1538-4365/aaf651
J. A. Tyson and the LSST Collaboration, “Large Synoptic Survey Telescope: Overview,” 2003. https://doi.org/10.1117/12.456772
M. Duarte, L. M. Sampedro, and A. Molino, “The S-PLUS: the star/galaxy classification based on a Machine Learning approach,” 2018. https://arxiv.org/pdf/1805.02427.pdf
R. E. González, R. P. Muñoz, and C. A. Hernández, “Galaxy detection and identification using deep learning and data augmentation,” 2018. https://doi.org/10.1016/j.ascom.2018.09.004
R. Lupton et al, “Preparing Red-Green-Blue (RGB) Images from CCD Data,” 2003. https://doi.org/10.1086/382245
S. Dieleman, K. W. Willett, and J. Dambre, “Rotation-invariant convolutional neural networks for galaxy morphology prediction,” 2015. https://doi.org/10.1093/mnras/stv632
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” 2017. https://doi.org/10.1109/CVPR.2017.634
Publicado
28/10/2019
Como Citar
MARTINAZZO, Ana; HIRATA, Nina Sumiko Tomita.
Multiband image classification of astronomical objects. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 140-143.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8314.
