Delta Sigma or Gamma theoretical covariance in TJPCov

[eduardojsbarroso]

Title

Implement theoretical shear covariance in TJPCov

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Description

In the cluster working group, we have two probes:

• Cluster number counts
• Shear from weak lensing

Currently, the covariance matrix uses:

• Cluster counts: theoretical or data-driven component
• Shear: data-driven only

There has been discussion about implementing a theoretical covariance for shear in TJPCov. An existing code from Heidi Wu might be useful (publications link), although the exact code link is not known.

Goal: start an implementation of a theoretical shear covariance in TJPCov, initially focusing on tangential shear γₜ, since this is the direction we are moving toward. The implementation should consider existing methods in Heidi Wu’s code where appropriate.

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References

• Cluster counts covariance examples:

  • covariance_cluster_counts.py
  • covariance_cluster_counts_ssc.py

• TJPCov stage usage in CLPipeline:
  tjpcov_pipeline.py

• Contacts for Heidi Wu’s code and possible initial implementation: Constantin Payerne and Yuanyuan Zhang

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Problem

We need a first implementation of theoretical shear covariance for TJPCov that:

• Supports γₜ (tangential shear) for now
• Can later be extended to ΔΣ if needed
• Takes into account relevant approaches from existing code by Heidi Wu

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Checklist

• Identify the theoretical covariance formulae and methods for shear
• Locate existing code from Heidi Wu or clarify with Constantin Payerne / Yuanyuan
• Implement a TJPCov module for theoretical shear covariance
• Integrate with the CLPipeline TJPCov stage and test functionality
• Document usage and examples for tangential shear covariance

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Type of change

• New feature (theoretical shear covariance)
• Refactoring / pipeline extension