Identifying Variability in Deeply Embedded Protostars with ALMA and CARMA
Bibliographic record
Abstract
Abstract Variability of pre-main-sequence stars observed at optical wavelengths has been attributed to fluctuations in the mass accretion rate from the circumstellar disk onto the forming star. Detailed models of accretion disks suggest that young deeply embedded protostars should also exhibit variations in their accretion rates, and that these changes can be tracked indirectly by monitoring the response of the dust envelope at mid-IR to millimeter wavelengths. Interferometers such as the Atacama Large Millimeter/submillimeter Array (ALMA) offer the resolution and sensitivity to observe small fluctuations in brightness at the scale of the disk where episodic accretion may be driven. In this work, we present novel methods for comparing interferometric observations and apply them to Combined Array for Research in Millimeter-wave Astronomy (CARMA) and ALMA 1.3 mm observations of deeply embedded protostars in Serpens taken 9 yr apart. We find no brightness variation above the limits of our analysis of a factor of ≳50%, due to the limited sensitivity of the CARMA observations and small number of sources common to both epochs. We further show that follow-up ALMA observations with a similar sample size and sensitivity may be able to uncover variability at the level of a few percent, and discuss implications for future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".