Identifying contaminants in astronomical images using convolutional neural networks
dc.contributor.author | PAILLASSA, M. | |
hal.structure.identifier | Department of Theoretical Physics [DTP] | |
dc.contributor.author | BERTIN, E. | |
hal.structure.identifier | M2A 2018 | |
dc.contributor.author | BOUY, H. | |
dc.date.issued | 2018 | |
dc.date.conference | 2018-07-03 | |
dc.description.abstractEn | In this work, we propose to use convolutional neural networks to detect contaminants in astronomical images. Each contaminant is treated in a one vs all fashion. Once trained, our network is able to detect various contaminants such as cosmic rays, hot and bad pixel defaults, persistence effects, satellite trails or fringe patterns in images of various field properties. The convolutional neural network is performing semantic segmentation: it can output a probability map, assigning to each pixel its probability to belong to the contaminant or the background class. Training and testing data have been gathered from real or simulated data. | |
dc.language.iso | en | |
dc.subject.en | convolutional neural networks | |
dc.subject.en | astronomical image analysis | |
dc.subject.en | astronomical image contaminants | |
dc.title.en | Identifying contaminants in astronomical images using convolutional neural networks | |
dc.type | Communication dans un congrès | |
dc.subject.hal | Planète et Univers [physics]/Astrophysique [astro-ph]/Instrumentation et méthodes pour l'astrophysique [astro-ph.IM] | |
bordeaux.country | FR | |
bordeaux.conference.city | bordeaux | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-01982125 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | non | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-01982125v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2018&rft.au=PAILLASSA,%20M.&BERTIN,%20E.&BOUY,%20H.&rft.genre=unknown |
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