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Fewer Mocks and Less Noise: Reducing the Dimensionality of Cosmological Observables with Subspace Projections
Creating accurate and low-noise covariance matrices represents a formidable challenge in modern-day cosmology. We present a formalism to compress arbitrary observables into a small number of bins by projection into a model-specific subspace that minimizes the prior-averaged log-likelihood error. The...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1103/PhysRevD.103.043508 http://cds.cern.ch/record/2730079 |