Estimators in development

Warning

All estimators on this page are not production-ready. Results have not been validated against published reference data. APIs and output formats may change without notice. Do not use them for scientific analysis.

The four stable estimators are: SMF, wp(rp), w(θ), and ΔΣ(rp). See Overview for a complete maturity table.

Luminosity function — LF φ(M)

Module: sum_stat.lf_smf (shared with SMF)

Estimators: 1/Vmax (luminosity_function_vmax()), SWML (luminosity_function_swml()), C⁻ (cumulative_luminosity_function_cminus())

Current status: The LF estimators are implemented and the code compiles and runs. They share the mathematical framework with the validated SMF estimators. The gap is a completed, automated validation against published LF measurements (e.g., COSMOS2015 from Ilbert+ 2013, GAMA from Loveday+ 2015).

What is needed before promotion to stable:

  • Automated comparison against COSMOS LF in ≥ 2 redshift bins, Δφ/φ < 10%.

  • Automated comparison against GAMA r-band LF, Δφ/φ < 10%.

  • Recovered Schechter α and M★ within 1σ from mock LF catalogues using 1/Vmax, SWML, and C⁻.

Relevant script: scripts/measure_joint_sumstat.py --stats LF

3D correlation function — ξ(r)

Module: sum_stat.twopcf (xi_r())

Current status: Implemented on top of treecorr (Landy-Szalay). Outputs shape-verified. Not validated against any published ξ(r) measurement.

What is needed before promotion to stable:

  • Recovery of a known power-law ξ(r) from a Poisson-sampled mock in a periodic box, to < 5% on scales 1–30 Mpc.

  • Comparison against a published ξ(r) measurement (e.g., BOSS LOWZ).

Redshift-space multipoles — ξℓ(s)

Module: sum_stat.twopcf (xi_multipoles())

Current status: Implemented (treecorr pair counts + JAX Legendre decomposition). Monopole–quadrupole ratio verified against linear theory at large scales in a single test.

What is needed before promotion to stable:

  • Recovery of monopole + quadrupole from an N-body snapshot with known RSD parameter β = f/b, to < 10%.

  • Comparison against a published ξℓ(s) from BOSS or eBOSS.

Angular power spectrum — Cℓ

Module: sum_stat.powspec (cl_angular())

Current status: Implemented with healpy pseudo-Cℓ. Output shape and unit verified. No validation run.

What is needed before promotion to stable:

  • Recovery of a known Cℓ from a Gaussian random field realisation, fractional error < 5% for ℓ = 10–500.

3D power spectrum — Pℓ(k)

Module: sum_stat.powspec (pk3d(), pk_multipoles())

Current status: Implemented with JAX FFT (FKP-weighted). Verified against known P(k) from a Gaussian random field at the 10% level. Not yet compared to a published measurement.

What is needed before promotion to stable:

  • End-to-end recovery of a ΛCDM P(k) from an N-body snapshot, within 5% on scales k = 0.05–0.5 h/Mpc.

  • Comparison against a published P(k) (e.g., BOSS DR12).

kNN cumulative distribution — Fk(r)

Module: sum_stat.knn (knn_cdf(), cross_knn_cdf(), knn_volume_map())

Current status: Implemented with pyfnntw (Rust kd-tree) + JAX. Unit tests pass (output shape, monotonicity, Poisson limit). Not yet run on any galaxy survey or simulation catalogue.

What is needed before promotion to stable:

  • Recovery of the Poisson kNN-CDF from a uniform random catalogue, residual < 1%.

  • Measurement on an Uchuu HOD mock and comparison against the Banerjee & Abel (2021) reference curves.

Redshift distributions — n(z)

Module: sum_stat.nz (nz_histogram(), nz_kde())

Current status: Histogram and KDE wrappers implemented and tested. Used internally by the lensing pipeline. Not documented or validated as a stand-alone product.

What is needed before promotion to stable:

  • Formal API stabilisation (bin-centre convention, normalisation).

  • Integration into the joint output HDF5 schema.

See also

Bibliography — full reference list for all estimators and surveys, with ADS links.