Side‐by‐Side Comparison of Fourier Transform Spectroscopy and Water Vapor Radiometry as Tools for the Calibration of Millimeter/Submillimeter Ground‐based Observatories
Bibliographic record
Abstract
Measurement techniques to monitor the atmospheric transmission at millimeter and submillimeter wavelengths are necessary for the operation of instruments such as the Atacama Large Millimeter Array (ALMA). Our previous Fourier transform spectroscopy (FTS) work at the Caltech Submillimeter Observatory (CSO) has shown that the atmospheric transmission spectrum can be accurately measured by this technique up to ∼1100 GHz with a time resolution of a few minutes. An alternative technique is water vapor radiometry, generally using a few channels around the 183 GHz H 2 O line that can provide much finer time resolution but relies upon models to translate the derived water vapor columns into spectrum predictions over the required frequency ranges. Time resolutions of the order of 1 s are necessary to carry out phase correction in ground-based mm/submm interferometry that can easily be reached by water vapor radiometers but not by FTS. Water vapor radiometry has the added advantages of being easier to operate and having lower costs than an FTS. In this context, we initiated a comparison campaign between the CSO FTS on Mauna Kea and a three-channel 183 GHz water vapor monitor (WVM) mounted on one of the antennas of the Sub-Millimeter Array (SMA), some 250 m away. The data presented here were taken on 2002 March 3 under very dry conditions (total precipitable water vapor zenith column, PWV, below 0.35 mm). The atmospheric transmission at microwaves (ATM) model described in a previous paper has been used to analyze the data. The primary conclusion is that for weather conditions allowing ground-based submillimeter interferometry to be carried out, the PWV can be measured with an agreement of about 0.01 mm between both instruments in timescales of several minutes, and therefore a combination of WVM plus an accurate mm/submm atmospheric model (based on extensive FTS work) provides a suitable tool for ALMA calibration in those conditions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".