Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data
- DOI
- 10.1109/tbdata.2019.2934475
- Published
- 2020-09-01
- Container
- IEEE Transactions on Big Data
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tbdata.2019.2934475,
title = {Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data},
author = {Radamanthys Stivaktakis and Grigorios Tsagkatakis and Bruno Moraes and Filipe Abdalla and Jean-Luc Starck and Panagiotis Tsakalides},
year = {2020},
journal = {IEEE Transactions on Big Data},
doi = {10.1109/tbdata.2019.2934475},
url = {https://doi.org/10.1109/tbdata.2019.2934475}
}RIS
TY - JOUR TI - Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data AU - Radamanthys Stivaktakis AU - Grigorios Tsagkatakis AU - Bruno Moraes AU - Filipe Abdalla AU - Jean-Luc Starck AU - Panagiotis Tsakalides PY - 2020 JO - IEEE Transactions on Big Data DO - 10.1109/tbdata.2019.2934475 UR - https://doi.org/10.1109/tbdata.2019.2934475 ER -
APA
Stivaktakis, R., Tsagkatakis, G., Moraes, B., Abdalla, F., Starck, J., & Tsakalides, P. (2020). Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data. IEEE Transactions on Big Data. https://doi.org/10.1109/tbdata.2019.2934475
Source records
- crossref · retrieved 2026-11-10T20:36:15.891Z