The JCMT Transient Survey: Identifying Submillimeter Continuum Variability over Several Year Timescales Using Archival JCMT Gould Belt Survey Observations
Bibliographic record
Abstract
Abstract Investigating variability at the earliest stages of low-mass star formation is fundamental in understanding how a protostar assembles mass. While many simulations of protostellar disks predict non-steady accretion onto protostars, deeper investigation requires robust observational constraints on the frequency and amplitude of variability events characterized across the observable SED. In this study, we develop methods to robustly analyze repeated observations of an area of the sky for submillimeter variability in order to determine constraints on the magnitude and frequency of deeply embedded protostars. We compare 850 μm JCMT Transient Survey data with archival JCMT Gould Belt Survey data to investigate variability over 2–4 year timescales. Out of 175 bright, independent emission sources identified in the overlapping fields, we find seven variable candidates, five of which we classify as Strong, and the remaining two we classify as Extended to indicate that the latter are associated with larger-scale structure. For the Strong variable candidates, we find an average fractional peak brightness change per year of ∣ 4.0 ∣ % yr − 1 , with a standard deviation of 2.7 % yr − 1 . In total, 7% of the protostars associated with 850 μm emission in our sample show signs of variability. Four of the five Strong sources are associated with a known protostar. The remaining source is a good follow-up target for an object that is anticipated to contain an enshrouded, deeply embedded protostar. In addition, we estimate the 850 μm periodicity of the submillimeter variable source, EC 53, to be 567 ± 32 days, based on the archival Gould Belt Survey data.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".