Alcock–Paczyński effect on void-finding
Bibliographic record
Abstract
Under the assumption of statistical isotropy, and in the absence of directional selection effects, a stack of voids is expected to be spherically symmetric, which makes it an excellent object to use for an Alcock–Paczyński (AP) test. This test is commonly carried out using the void-galaxy cross-correlation function (CCF), which has emerged as a competitive probe, especially in combination with the galaxy-galaxy auto-correlation function. Current studies of the AP effect around voids assume that void-centre positions are influenced by the choice of fiducial cosmology in the same way as galaxy positions. We show that this assumption, though prevalent in the literature, is complicated by the response of void-finding algorithms to shifts in tracer positions. Using stretched simulation boxes to emulate the AP effect, we investigate how the void-galaxy CCF changes due to its presence, revealing an additional effect imprinted in the CCF that must be accounted for. The effect originates from the response of void finders to the distorted tracer field – which leads to reduction of the amplitude of the AP signal in the CCF – and thus depends on the specific void-finding algorithm used. We present results for four different void-finding packages, namely <ns0:monospace>REVOLVER</ns0:monospace>, <ns0:monospace>VIDE</ns0:monospace>, <ns0:monospace>voxel</ns0:monospace>, and the spherical void finder in the <ns0:monospace>Pylians3</ns0:monospace> library, demonstrating how incorrect treatment of the AP effect results in biases in the recovered parameters, regardless of the technique used. Finally, we propose a method to alleviate this issue without resorting to complex and finder-specific modelling of the void-finder response to AP.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".