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dc.contributor.author
Gebhard, Timothy D.
dc.contributor.author
Bonse, Markus J.
dc.contributor.author
Quanz, Sascha Patrick
dc.contributor.author
Schölkopf, Bernhard
dc.date.accessioned
2021-03-25T12:44:09Z
dc.date.available
2021-03-25T12:33:17Z
dc.date.available
2021-03-25T12:44:09Z
dc.date.issued
2020-12-11
dc.identifier.uri
http://hdl.handle.net/20.500.11850/476321
dc.description.abstract
The detection of exoplanets in high-contrast imaging (HCI) data hinges on post-processing methods to remove spurious light from the host star. So far, existing methods for this task hardly utilize any of the available domain knowledge about the problem explicitly. We propose a new approach to HCI post-processing based on a modified half-sibling regression scheme, and show how we use this framework to combine machine learning with existing scientific domain knowledge. On three real data sets, we demonstrate that the resulting system performs clearly better (both visually and in terms of the SNR) than one of the currently leading algorithms. If further studies can confirm these results, our method could have the potential to allow significant discoveries of exoplanets both in new and archival data.
en_US
dc.language.iso
en
en_US
dc.publisher
ML4PS
en_US
dc.title
Physically constrained causal noise models for high-contrast imaging of exoplanets
en_US
dc.type
Conference Paper
ethz.size
9 p.
en_US
ethz.event
3rd Workshop on Machine Learning and Physical Science (ML4PS) at at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) (virtual)
en_US
ethz.event.location
Vancouver, Canada
en_US
ethz.event.date
December 11, 2020
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the workshop was conducted virtually.
en_US
ethz.identifier.arxiv
2010.05591v2
ethz.publication.place
s.l.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02010 - Dep. Physik / Dep. of Physics::02532 - Institut für Teilchen- und Astrophysik / Inst. Particle Physics and Astrophysics::09680 - Quanz, Sascha Patrick / Quanz, Sascha Patrick
en_US
ethz.identifier.url
https://ml4physicalsciences.github.io/2020/
ethz.relation.isNewVersionOf
20.500.11850/461985
ethz.date.deposited
2021-03-25T12:33:26Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-03-25T12:44:21Z
ethz.rosetta.lastUpdated
2021-03-25T12:44:21Z
ethz.rosetta.versionExported
true
ethz.COinS
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