Validation

Beyond the internal unit and analytic tests, MeasureIA's estimators are cross-validated against several independent, publicly available correlation-function codes. This gives an end-to-end check that the full pipelines — pair counting, estimators, multipole integration and jackknife covariance — agree with established implementations, for both the box and the lightcone.

Approach

• Mock catalogues (measureia.mocks): all comparisons run on a seeded synthetic catalogue with a strong, known radial-alignment signal, so ratios are meaningful in every bin. Both codes read byte-identical inputs.
• Runnable scripts (validation/run_*.py): one per comparison. Each always computes the MeasureIA side; if the external package is installed it also computes the external side and writes it to validation/reference_outputs/, otherwise it compares against the committed reference file.
• Enforced in CI (tests/test_validation_references.py): the committed reference outputs are compared against MeasureIA at fixed tolerances, so the cross-package agreement is checked on every test run without needing the external packages installed.

What has been validated

Comparison	External code	Quantities	Agreement
Box \(w_{gg}\), \(w_{g+}\)	halotools	projected \(w\)	machine precision (\(2\times10^{-13}\) / \(4\times10^{-15}\))¹
Box multipoles	corr_pc	\(\xi(r,\mu)\) grid + \(\tilde\xi_{gg,0}\), \(\tilde\xi_{g+,2}\)	\(\le5\times10^{-6}\) grid, \(\sim10^{-6}\) multipoles¹
Lightcone \(w\)	treecorr	\(w_{gg}\), \(w_{g+}\)	\(\sim10^{-5}\) (\(g+\)), \(\le0.4\%\) (\(gg\))
Lightcone \(w\)	corr_pc	\(w_{gg}\), \(w_{g+}\)	\(\le0.15\%\) (\(g+\)), \(\le0.4\%\) (\(gg\))
Lightcone multipoles	corr_pc	\(\tilde\xi_{gg,0}\), \(\tilde\xi_{g+,2}\)	\(\le0.2\%\) / \(\le0.3\%\)
Box ↔ lightcone (plane-parallel)	— (self-consistency)	pair counts, \(w\)	DD \(<1\%\); residuals fully attributed²
Box jackknife (delete-one identity)	— (self-consistency)	realisations, cov	machine precision (\(\le10^{-12}\))
Box jackknife covariance	corr_pc	\(w\) + multipole cov	realisations \(\le5\times10^{-5}\), std \(\le5\times10^{-7}\)
Lightcone jackknife covariance	treecorr	\(w\) cov	std \(\le5\times10^{-5}\) (\(g+\)), \(\le0.6\%\) (\(gg\))
Lightcone jackknife covariance	corr_pc	\(w\) + multipole cov	realisations \(\le3\times10^{-4}\)

¹ Agreement is exact up to floating point / the external code's output precision, once the responsivity \(2\mathcal{R}\) factor is accounted for (MeasureIA divides \(S_+\) terms by \(2\mathcal{R}\); halotools and corr_pc do not — see Conventions). ² The plane-parallel box↔lightcone difference is understood: analytic randoms (periodic box) versus empirical randoms (bounded window), plus the box/lightcone estimator and responsivity differences.

The lightcone comparisons also confirm the e1/e2 shear convention and chirality documented on the Conventions page (treecorr needs only the standard IA flip \(g \to -g\)).

Running the validations yourself

The comparison scripts live in validation/ in the repository rather than in the installed package, so start from a clone (see Installation) and install the pip-available external packages with it:

git clone https://github.com/MarloesvL/measure_IA.git
cd measure_IA
uv sync --extra validation            # or: pip install -e ".[validation]"  -- halotools + treecorr

Then run the enforced cross-package checks (these use the committed reference outputs):

uv run pytest tests/test_validation_references.py

Or run an individual comparison script, which will use the external package if it is installed and otherwise compare against the committed reference:

uv run python validation/run_box_halotools.py
uv run python validation/run_lightcone_treecorr.py

The corr_pc comparisons (Singh 2021, corr_pc on GitHub) require building the C++ code separately. The full build recipe (including a no-MPI stub and two small patches) and the detailed per-comparison convention notes are in validation/README.md in the repository.