Thermal susceptibility study of the Canadian Hydrogen Intensity Mapping Experiment Pathfinder Instruments
Bibliographic record
Abstract
The Canadian Hydrogen Intensity Mapping Experiment (CHIME) will study Baryon Acoustic Oscillations (BAO) in the redshift range when the expansion of the Universe began to accelerate due Dark Energy’s dominating influence. CHIME will measure the Hubble parameter, H(z), and constrain the equation of state parameter, w, of Dark Energy. These measurements are critical in furthering our understanding of the expansion history of the Universe and Dark Energy. CHIME will observe the faint cosmological signal and map expansion from its location at the Dominion Radio Astrophysical Observatory (DRAO) near Penticton, BC. In order to make the required measurements, the CHIME telescope requires an accurate calibration plan. Of the many components of the overall calibration plan, this paper specifically addresses the system gain calibration, with respect to exploring the relationship between system gain and temperature in the development of a thermal model. The model presented here describes the relationship to first order and lays the foundation for more detailed study. Findings provide insight into system gain configuration and facilitate subsequent development of the thermal model. The results presented here are critical steps in the system gain calibration, contributing to a successful overall calibration plan that will ultimately lead to reliable data from which new science results will emerge. The analysis of these data has the potential to lead to an increased understanding of the expansion of the Universe and the nature of Dark Energy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".