SANEPIC: A Mapmaking Method for Time Stream Data from Large Arrays
Bibliographic record
Abstract
We describe a mapmaking method that we have developed for the Balloon-borne Large Aperture Submillimeter Telescope (BLAST) experiment, but which should have general application to data from other submillimeter arrays. Our method uses a maximum likelihood-based approach, with several approximations, which allows images to be constructed using large amounts of data with fairly modest computer memory and processing requirements. This new approach, Signal and Noise Estimation Procedure Including Correlations (SANEPIC), builds on several previous methods but focuses specifically on the regime where there are a large number of detectors sampling the same map of the sky, and explicitly allowing for the possibility of strong correlations between the detector time streams. We provide real and simulated examples of how well this method performs compared with more simplistic mapmakers based on filtering. We discuss two separate implementations of SANEPIC: a brute-force approach, in which the inverse pixel-pixel covariance matrix is computed, and an iterative approach, which is much more efficient for large maps. SANEPIC has been successfully used to produce maps using data from the 2005 BLAST flight.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".