Probing the physics of star formation (ProPStar)
Bibliographic record
Abstract
Context . Electron fraction and cosmic-ray ionization rates in star-forming regions are important quantities in astrochemical modeling and are critical to the degree of coupling between neutrals, ions, and electrons, which regulates the dynamics of the magnetic field. However, these are difficult quantities to estimate. Aims . We aim to derive the electron fraction and cosmic-ray ionization rate maps of an active star-forming region. Methods . We combined observations of the nearby NGC 1333 star-forming region carried out with the NOEMA interferometer and IRAM 30 m single dish to generate high spatial dynamic range maps of different molecular transitions. We used the DCO + and H 13 CO + ratio (in addition to complementary data) to estimate the electron fraction and produce cosmic-ray ionization rate maps. Results . We derived the first large-area electron fraction and cosmic-ray ionization rate resolved maps in a star-forming region, with typical values of 10 −65 and 10 −16.5 s −1 , respectively. The maps present clear evidence of enhanced values around embedded young stellar objects (YSOs). This provides strong evidence for locally accelerated cosmic rays. We also found a strong enhancement toward the northwest region in the map that might be related either to an interaction with a bubble or to locally generated cosmic rays by YSOs. We used the typical electron fraction and derived a magnetohydrodynamic (MHD) turbulence dissipation scale of 0.054 pc, which could be tested with future observations. Conclusions . We found a higher cosmic-ray ionization rate compared to the canonical value for N (H 2 ) = 10 21 −10 23 cm −2 of 10 −17 s −1 in the region, and it is likely generated by the accreting YSOs. The high value of the electron fraction suggests that new disks will form from gas in the ideal-MHD limit. This indicates that local enhancements of ζ (H 2 ), due to YSOs, should be taken into account in the analysis of clustered star formation.
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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 | 0.000 |
| 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) | 0.002 | 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".