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A machine learns.pdf
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🔗 Source: oro.open.ac.uk
📊 Size: 2.75 MB
👤 Author: Perger M., Anglada-Escudé G., Baroch D., Lafarga M., Ribas I., Morales J. C., Herrero E., Amado P. J
⬇️ Downloads: 167

Summary

A machine learning approach is used to correct radial velocities using physical observables, aiming to reduce spurious Doppler variability caused by stellar activity. The method involves training a deep neural network algorithm on simulated observations of two known stars, ϵ Eridani and AU Microscopii, to predict Doppler corrections. The results show that the approach can drastically reduce spurious Doppler variability from known physical effects, and the correction matches the observed variability in real observations. However, the noise reduction is not as good as in simulations, likely due to a lack of detail in the simulated physics. The study demonstrates the potential of a model-driven machine learning approach to clean Doppler signals from activity-induced variability, and suggests that using additional indicators, more complete models, and optimized sampling strategies can lead to significant improvements.

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Description

A machine learning approach is used to correct radial velocities using physical observables, aiming to reduce spurious Doppler variability caused by stellar...

Technical Information

  • File Format: PDF
  • File Size: 2.75 MB
  • Pages: 16
  • Language: EN
  • Author: Perger M., Anglada-Escudé G., Baroch D., Lafarga M., Ribas I., Morales J. C., Herrero E., Amado P. J
  • Total Downloads: 167
  • Last Updated: 1 week ago

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