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pro vyhledávání: '"Connick Kathleen"'
In this follow-up paper, we investigate the use of Convolutional Neural Network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously, we have constructed a Neural Network architecture suitable for the der
Externí odkaz:
http://arxiv.org/abs/2210.17470
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep Learning te
Externí odkaz:
http://arxiv.org/abs/2201.12476
Publikováno v:
Open Astronomy, Vol 32, Iss 1, Pp 1031-1037 (2023)
In this follow-up article, we investigate the use of convolutional neural network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously, we have constructed a Neural Network architecture suitable for the d
Externí odkaz:
https://doaj.org/article/6e95ae1e2d814704a670cbf526ffb44d
Publikováno v:
Open Astronomy, Vol 31, Iss 1, Pp 38-57 (2022)
Machine learning is an efficient method for analysing and interpreting the increasing amount of astronomical data that are available. In this study, we show a pedagogical approach that should benefit anyone willing to experiment with deep learning te
Externí odkaz:
https://doaj.org/article/eb32f4eb2caf4c2aa6f87706d6474d61
We are applying various ML/DL techniques for the purpose of stellar spectroscopy. Having already ran tests with Principal Component Analysis (PCA) and Sliced Inverse Regression (SIR), we now turn our focus to Convolution Neural Network (CNN), among o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3b0bb7b275f5c3fa6a1c0a07bee0278