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Summary Statistics: Final Data Products

The Summary Statistics module produces final data products for likelihood analysis.

This module completes the cosmological pipeline by converting tracers and power spectra into statistical quantities for comparison with observations. Currently, cloelib supports observables for large-scale structure multi-probe experiments. Observable outputs are formatted — or can be requested to be formatted — following the cosmolib data format, which is used by downstream packages such as the reading and writing package euclidlib. To understand the format, we kindly request the user to check euclidlib docs.

To see examples of usage, please check the example notebooks for observable computation at playground.

Overview

Location: cloelib/summary_statistics/

This module computes final statistical quantities for likelihood evaluation, including:

  • \(C_\ell\): Angular power spectra for photometric surveys, either full sky or convolved with the mask
  • \(\xi_+(\theta)\), \(\xi_-(\theta)\), \(w(\theta)\), \(\gamma_T\), \(\gamma_\times\): Angular two-point photometric correlation functions
  • COSEBIs: Complete Orthogonal Sets of E/B-Integrals for photometric surveys as in Asgari et al., 2018.
  • \(P_\ell(k)\): Legendre multipoles for spectroscopic surveys, either full sky or convolved with the mask
  • \(\xi(r)\): Two-point correlation function from Legendre multipoles, also supported as polar two-point correlation function
  • \(\alpha_\parallel\), \(\alpha_\perp\): BAO distortion parameters for spectroscopic surveys

These quantities are directly measurable and form the basis for cosmological parameter inference.

!!! warning cloelib does not use internal interpolations. Keep redshift and wavenumber arrays to a maximum of 1500 elements for optimal performance. Otherwise, memory problems may arise.

Performance Tips

cloelib does not use internal interpolations. Keep redshift and wavenumber arrays to a maximum of 1500 elements for optimal performance. Otherwise, memory problems may arise.

Performance Tips

For expensive summary-statistic evaluations, prefer moderate redshift and wavenumber grids, especially when scanning parameter space repeatedly. In practice, keeping these arrays at or below roughly 1500 elements avoids unnecessary memory pressure in the current implementation.

Available Summary Statistics

Next Steps

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