One-Point Matter PDF’s Beyond TopHat Filters
Bibliographic record
Abstract
In this thesis, I studied the one-point probability distribution function (PDF) for averaged matter densities over spherical cells, which can be used to non-perturbatively probe the large-scale structure of our universe. The PDF depends on a function, known as the filter/window function, which takes some weighted average over the observed matter density within each cell. This averaging allows one to study the density field as some smoothed function rather than discrete points. In order to consider filters of different kinds, the PDF’s are constructed numerically using Python code. The PDF is analytically modeled using a path integral framework. By considering a family of radial window functions interpolating between the TopHat and Gaussian filters in coordinate space, I investigated the sensitivity of the PDF to the shape of the window function. It was found that the sensitivity is rather mild suggesting that the PDF is robust against the precise choice of the filter. Effective field theory (EFT) corrections were included and used to examine how sensitive different filters are to short-scale physics. Similar to the PDF, the effects coming from short-scale physics appeared weakly dependent on the choice of filter, regardless of how smooth the filter’s boundary was. The contribution coming from aspherical fluctuations to the collapse dynamics of the cell were computed by comparing the numerical PDF to high-resolution N-body simulations. It was found that this contribution factorizes as a prefactor to the PDF, which is redshift independent, with the exception of smaller sized cells which display some mild redshift dependent shifting. These discrepancies are thought to be associated with two-loop corrections to the PDF. We expect this model to be flexible enough to study beyond the ΛCDM model and act as a probe for new fundamental physics.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".