Submillimeter and Mid-Infrared Variability of Young Stellar Objects in the M17SWex Intermediate-mass Star-forming Region
Bibliographic record
Abstract
Abstract We present a comprehensive analysis of young stellar object (YSO) variability within the M17 Southwest Extension (M17 SWex), using 3.5 yr of monitoring data from the James Clerk Maxwell Telescope (JCMT) Transient Survey at submillimeter and 9 yr from the NEOWISE mission at mid-infrared (mid-IR). Our study encompasses observations of 147 bright submillimeter peaks identified within our deep JCMT coadded map as well as 156 YSOs in NEOWISE W1 and 179 in W2 that were previously identified in Spitzer surveys. We find three robust submillimeter variables: two are candidate YSOs and one is a likely extragalactic source. At mid-IR wavelengths, our analysis reveals secular and stochastic variability in 47 YSOs, with the highest fraction of secular variability occurring at the earliest evolutionary stage. This is similar to what has previously been observed for low-mass YSO variability within the Gould Belt. However, we observe less overall variability in M17 SWex at both the submillimeter and mid-IR. We suspect that this lower fraction is due to the greater distance to M17 SWex. Our findings showcase the utility of multiwavelength observations to better capture the complex variability phenomena inherent to star formation processes and demonstrate the importance of years-long monitoring of a diverse selection of star-forming environments.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".