Forecasting galaxy cluster H <scp>i</scp> mass recovery with CHIME at redshifts z = 1 and 2 via the IllustrisTNG simulations
Bibliographic record
Abstract
ABSTRACT The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a drift-scan interferometer designed to map the entire northern sky every 24 h. The all-sky coverage and sensitivity to neutral hydrogen flux at intermediate redshifts makes the instrument a resource for other exciting science in addition to cosmology for which it was originally designed. Here, we demonstrate its utility for the study of the H i content of galaxy populations across environments and redshifts. We use simulated data from the IllustrisTNG project to generate mock CHIME-like intensity maps, which we cross-correlate with various tracers – including galaxies and galaxy clusters – to recover aggregate H i signals from stacking analyses. We find that there is more flux in stacks on galaxy clusters or cluster member galaxies compared to those on a general galaxy catalogue due to the enhanced number of H i-rich sources included in the CHIME primary beam. We report that it is possible to infer an average $M_\mathrm{HI}$ for clusters as a function of redshift and selection criteria from the signal in their averaged stacks despite the instrument’s low spatial resolution. This proof-of-concept result opens up a promising, and timely, new avenue to measure the evolution of the neutral hydrogen content in intermediate-to-high redshift galaxy clusters via cross-correlation of galaxy cluster catalogues with 21-cm intensity maps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".