BLAST: Beyond Limber Angular power Spectra Toolkit. A fast and efficient algorithm for 3x2 pt analysis
Bibliographic record
Abstract
The advent of next-generation photometric and spectroscopic surveys is approaching, bringing more data with tighter error bars. As a result, theoretical models will become more complex, incorporating additional parameters, which will increase the dimensionality of the parameter space and make posteriors more challenging to explore. Consequently, the need to improve and speed up our current analysis pipelines will grow. In this work, we focus on the 3x2pt statistics, a summary statistic that has become increasingly popular in recent years due to its great constraining power. These statistics involve calculating angular two-point correlation functions for the auto- and cross-correlations between galaxy clustering and weak lensing. The corresponding model is determined by integrating the product of the power spectrum and two highly-oscillating Bessel functions over three dimensions, which makes the evaluation particularly challenging. Typically, this difficulty is circumvented by employing the so-called Limber approximation, which is an important source of error. We present BLAST, an innovative and efficient algorithm for calculating angular power spectra without employing the Limber approximation or assuming a scale-dependent growth rate, based on the use of Chebyshev polynomials. The algorithm is compared with the publicly available beyond-Limber codes, whose performances were recently tested by the Rubin Observatory Legacy Survey of Space and Time Dark Energy Science Collaboration. At similar accuracy, BLAST is $\approx 10$-$15 \times$ faster than the winning method of the challenge, also showing excellent scaling with respect to various hyper-parameters. BLAST is publicly available on GitHub, and we release a repository where we explain how to use the code.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.036 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".