Bias-free shear estimation using artificial neural networks

@article{Gruen2010BiasfreeSE,
  title={Bias-free shear estimation using artificial neural networks},
  author={Daniel Gruen and Stella Seitz and Johannes Koppenhoefer and A. Riffeser},
  journal={The Astrophysical Journal},
  year={2010},
  volume={720},
  pages={639-651}
}
Bias due to imperfect shear calibration is the biggest obstacle when constraints on cosmological parameters are to be extracted from large area weak lensing surveys such as Pan-STARRS-3{pi}, DES, or future satellite missions like EUCLID. We demonstrate that bias present in existing shear measurement pipelines (e.g., KSB) can be almost entirely removed by means of neural networks. In this way, bias correction can depend on the properties of the individual galaxy instead of being a single global… Expand

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