Carbon chain diversity in L1544 and IRAS 16293–2422: an astrochemical pathfinder study for the SKAO
Bibliographic record
Abstract
ABSTRACT Astrochemical observations have revealed a surprisingly high level of chemical complexity, including long carbon chains, in the earliest stages of Sun-like star formation. The origin of these species and whether they undergo further growth, possibly contributing to the molecular complexity of planetary systems, remain open questions. We present recent observations performed using the 100-m Green Bank Telescope of the prestellar core L1544, and the protostellar system IRAS 16293–2422. In L1544, we detected several complex carbon-bearing species, including C$_2$S, C$_3$S, C$_3$N, c-C$_3$H, C$_4$H, and C$_6$H, complementing previously reported emission of cyanopolyynes. In IRAS 16293–2422, we detected c-C$_3$H and, for the first time, HC$_7$N. Thanks to the high spectral resolution, we refine the rest frequencies of several c-C$_3$H and C$_6$H transitions. We perform radiative transfer analysis, highlighting a chemical difference between the two sources: IRAS 16293–2422 shows column densities 10 to 100 times lower than L1544. We perform astrochemical modelling, employing an up-to-date chemical network with revised reaction rates. Models reproduce the general trends, with cyanopolyyne and polyynyl radical abundances decreasing as molecular size increases, but underestimate the abundances of cyanopolyynes longer than HC$_5$N by up to two orders of magnitude. Current models, which include the dominant neutral–neutral formation routes, cannot account for this discrepancy, suggesting that the chemical network is incomplete. We propose that additional ion–molecule reactions are crucial for the formation of these species. Developing a more comprehensive chemical network for long carbon chains is essential for accurately interpreting present and future observations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".