Toward a multitracer neutrino mass measurement with line-intensity mapping
Bibliographic record
Abstract
Accurately determining neutrino masses is a main objective of contemporary cosmology. Since massive neutrinos affect structure formation and evolution, probes of large scale structure are sensitive to the sum of their masses. In this work, we explore future constraints on $\ensuremath{\sum}{m}_{\ensuremath{\nu}}$ utilizing line-intensity mapping (LIM) as a promising emerging probe of the density of our Universe, focusing on the fine-structure [CII] line as an example, and compare these constraints with those derived from traditional galaxy surveys. Additionally, we perform a multitracer analysis using velocity tomography via the kinetic Sunyaev-Zeldovich and moving lens effects to reconstruct the three-dimensional velocity field. Our forecasts indicate that the next-generation AtLAST detector by itself can achieve ${\ensuremath{\sigma}}_{\mathrm{\ensuremath{\Sigma}}{m}_{\ensuremath{\nu}}}\ensuremath{\sim}50\text{ }\text{ }\mathrm{meV}$ sensitivity. Velocity tomography will further improve these constraints by 4%. Incorporating forecasts for CMB-S4 and DESI-BAO in a comprehensive multitracer analysis, while setting a prior on the optical depth to reionization $\ensuremath{\tau}$ derived using 21-cm forecasted observations, to break degeneracies, we find that a $\ensuremath{\gtrsim}5\ensuremath{\sigma}$ detection of $\ensuremath{\sum}{m}_{\ensuremath{\nu}}\ensuremath{\sim}60\text{ }\text{ }\mathrm{meV}$, under the normal hierarchy, is within reach with LIM. Even without a $\ensuremath{\tau}$ prior, our combined forecast reaches ${\ensuremath{\sigma}}_{\mathrm{\ensuremath{\Sigma}}{m}_{\ensuremath{\nu}}}\ensuremath{\sim}18\text{ }\text{ }\mathrm{meV}$.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".