The JCMT Transient Survey: Six Year Summary of 450/850 μm Protostellar Variability and Calibration Pipeline Version 2.0
Bibliographic record
Abstract
Abstract The James Clerk Maxwell Telescope (JCMT) Transient Survey has been monitoring eight Gould Belt low-mass star-forming regions since 2015 December and six somewhat more distant intermediate-mass star-forming regions since 2020 February with the Submillimeter Common User Bolometer Array 2 on board JCMT at 450 and 850 μm and with an approximately monthly cadence. We introduce our pipeline v2 relative calibration procedures for image alignment and flux calibration across epochs, improving on our previous pipeline v1 by decreasing measurement uncertainties and providing additional robustness. These new techniques work at both 850 and 450 μm, where version 1 only allowed investigation of the 850 μm data. Pipeline v2 achieves better than 0.″5 relative image alignment, less than a tenth of the submillimeter beam widths. The version 2 relative flux calibration is found to be 1% at 850 μm and <5% at 450 μm. The improvement in the calibration is demonstrated by comparing the two pipelines over the first 4 yr of the survey and recovering additional robust variables with version 2. Using the full 6 yr of the Gould Belt survey, the number of robust variables increases by 50%, and at 450 μm we identify four robust variables, all of which are also robust at 850 μm. The multiwavelength light curves for these sources are investigated and found to be consistent with the variability being due to dust heating within the envelope in response to accretion luminosity changes from the central source.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".