Coupling parsec and gigaparsec scales: Primordial non-Gaussianity with multitracer intensity mapping
Bibliographic record
Abstract
Primordial non-Gaussianity (PNG) is a key probe of the origins of primordial fluctuations in the early universe. It has been shown that multitracer measurements of large-scale structure can produce high-precision measurements of PNG. Future line intensity mapping surveys are well suited to these measurements owing to their ability to rapidly survey large volumes and access the large scales at which PNG becomes important. In this paper, we explore for the first time how multitracer PNG measurements with intensity mapping surveys depend on the sub-galactic scale physics which drives line emission. We consider an example cross-correlation between CO maps, and quantify the impact varying the astrophysical model has on ${f}_{\mathrm{NL}}$ measurements. We find a nontrivial coupling between horizon-scale PNG measurements and the molecular cloud-scale interstellar medium that can have order unity effects on ${f}_{\mathrm{NL}}$ constraints with near-future experimental sensitivities. We discuss how these effects depend on noise level and survey design. We further find that the cross-correlation shot noise, an effect nearly unique to intensity mapping measurements, can play an important role in multi-tracer analyses and should not be neglected.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".