Far-infrared Luminosity Bursts Trace Mass Accretion onto Protostars
Bibliographic record
Abstract
Abstract Evidence abounds that young stellar objects undergo luminous bursts of intense accretion that are short compared to the time it takes to form a star. It remains unclear how much these events contribute to the main-sequence masses of the stars. We demonstrate the power of time-series far-infrared (far-IR) photometry to answer this question compared to similar observations at shorter and longer wavelengths. We start with model spectral energy distributions that have been fit to 86 Class 0 protostars in the Orion molecular clouds. The protostars sample a broad range of envelope densities, cavity geometries, and viewing angles. We then increase the luminosity of each model by factors of 10, 50, and 100 and assess how these luminosity increases manifest in the form of flux increases over wavelength ranges of interest. We find that the fractional change in the far-IR luminosity during a burst more closely traces the change in the accretion rate than photometric diagnostics at mid-infrared and submillimeter wavelengths. We also show that observations at far-IR and longer wavelengths reliably track accretion changes without confusion from large, variable circumstellar and interstellar extinction that plague studies at shorter wavelengths. We close by discussing the ability of a proposed far-IR surveyor for the 2030s to enable improvements in our understanding of the role of accretion bursts in mass assembly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".