Physically constrained causal noise models for high-contrast imaging of exoplanets
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Date
2020-12-11Type
- Conference Paper
ETH Bibliography
yes
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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. Show more
Publication status
publishedPublisher
ML4PSEvent
Organisational unit
09680 - Quanz, Sascha Patrick / Quanz, Sascha Patrick
Related publications and datasets
Is new version of: http://hdl.handle.net/20.500.11850/461985
Notes
Due to the Coronavirus (COVID-19) the workshop was conducted virtually.More
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ETH Bibliography
yes
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