Wiener filtering and multi-tracer techniques for dark matter cross-correlations between gamma-ray emission and galaxy catalogs
Bibliographic record
Abstract
Abstract Cross-correlations between a gravitational tracer of dark matter and the contribution to the unresolved gamma-ray background (UGRB) from the radiation produced by the annihilation of the particles responsible for the dark matter, have been established as a powerful tool to investigate the particle physics nature of dark matter. Cross-correlations of the UGRB with galaxy catalogs, cluster catalogs and weak lensing have indeed been measured. In this paper we study statistical techniques that could improve the sensitivity of the cross-correlation techniques on the bounds that can be set to the particle dark matter physical properties. The two methods that we investigate are the application of a Wiener filter and the exploitation of the full multi-tracer information. After identifying the optimal strategies, we show that the adoption of a Wiener filter in the cross-correlation analysis can improve the sensitivity to the dark matter annihilation rate by a factor ranging from 2 to 2.5 as compared to the standard analysis where no filter is applied. The inclusion of the full multi-tracer information can improve the sensitivity up to a factor of 5 for dark matter masses below about 50 GeV, the Wiener filter remaining the best option for heavier dark matter.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".