Tracking ALMA System Temperature with Water Vapor Data at High Frequency
Bibliographic record
Abstract
Abstract As the world-leading submillimeter telescope, the Atacama Large Millimeter/submillimeter Array observatory is now putting more focus on high-frequency observations at Band 7–10 (frequencies from 275 to 950 GHz). However, high-frequency observations often suffer from rapid variations in atmospheric opacity that directly affect the system temperature T sys. Current observations perform discrete atmospheric calibrations (Atm-cals) every few minutes, with typically 10–20 occurring per hour for high frequency observation and each taking 30–40 s. In order to obtain more accurate flux measurements and reduce the number of atmospheric calibrations (Atm-cals), a new method to monitor T sys continuously is proposed using existing data in the measurement set. In this work, we demonstrate the viability of using water vapor radiometer (WVR) data to track the T sys continuously. We find a tight linear correlation between T sys measured using the traditional method and T sys extrapolated based on WVR data with scatter of 0.5%–3%. Although the exact form of the linear relation varies among different data sets and spectral windows, we can use a small number of discrete T sys measurements to fit the linear relation and use this heuristic relationship to derive T sys every 10 s. Furthermore, we successfully reproduce the observed correlation using atmospheric transmission at microwave modeling and demonstrate the viability of a more general method to directly derive the T sys from the modeling. We apply the semi-continuous T sys from heuristic fitting on a few data sets from Band 7 to Band 10 and compare the flux measured using these methods. We find the discrete and continuous T sys methods give us consistent flux measurements with differences up to 5%. Furthermore, this method has significantly reduced the flux uncertainty due to T sys variability for one data set, which has large precipitable water vapor fluctuation, from 10% to 0.7%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".