Separating angular and radial modes with spherical-Fourier Bessel power spectrum on all scales and implications for systematics mitigation
Bibliographic record
Abstract
Current and upcoming large-scale structure surveys place stringent requirements on the mitigation of observational systematics in order to achieve their unprecedented constraining power. In this work, we investigate the potential use of the spherical Fourier-Bessel (SFB) power spectrum in controlling systematics, leveraging its capability of disentangling angular and radial scales. We first clarify how the discrete SFB basis describes radial scales via the index $n$ and demonstrate that the SFB power spectrum reduces to the clustering wedge $P(k,μ)$ in the plane-parallel limit, enabling it to inherit results from past literature based on the clustering wedge. While the parallel and perpendicular Fourier mode $(k_{||}, k_\perp)$ decomposition underlying the wedge is only valid for surveys of small angular coverage with a well-defined global line-of-sight, the SFB basis provides a natural generalization that can be applied to the full sky. Crucially, the separation of angular and radial scales allows systematics to be localized in SFB space. In particular, systematics with broad and smooth radial distributions primarily concentrate in the $n=0$ modes corresponding to the largest radial scales. This localization behavior enables one to selectively remove only particular angular and radial modes contaminated by systematics. This is in contrast to standard 3D clustering analyses of wide-field surveys based on power spectrum multipoles, where systematic effects necessitate the removal of all modes below a given $k_{\rm min}$. Our findings advocate for adopting the SFB basis in 3D clustering analyses where systematics have become a limiting factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".