Astronomical Detection of the Interstellar Anion C<sub>10</sub>H<sup>−</sup> toward TMC-1 from the GOTHAM Large Program on the Green Bank Telescope
Bibliographic record
Abstract
Abstract Using data from the Green Bank Telescope (GBT) Observations of TMC-1: Hunting for Aromatic Molecules (GOTHAM) survey, we report the first astronomical detection of the C10H− anion. The astronomical observations also provided the necessary data to refine the spectroscopic parameters of C10H−. From the velocity stacked data and the matched filter response, C10H− is detected at >9σ confidence level at a column density of 4.04 − 2.23 + 10.67 × 10 11 cm−2. A dedicated search for the C10H radical was also conducted toward TMC-1. In this case, the stacked molecular emission of C10H was detected at a ∼3.2σ confidence interval at a column density of 2.02 − 0.82 + 2.68 × 10 11 cm−2. However, as the determined confidence level is currently <5σ, we consider the identification of C10H as tentative. The full GOTHAM data set was also used to better characterize the physical parameters including column density, excitation temperature, line width, and source size for the C4H, C6H, and C8H radicals and their respective anions, and the measured column densities were compared to the predictions from a gas/grain chemical formation model and from a machine learning analysis. Given the measured values, the C10H−/C10H column density ratio is ∼ 2.0 − 1.6 + 5.9 —the highest value measured between an anion and neutral species to date. Such a high ratio is at odds with current theories for interstellar anion chemistry. For the radical species, both models can reproduce the measured abundances found from the survey; however, the machine learning analysis matches the detected anion abundances much better than the gas/grain chemical model, suggesting that the current understanding of the formation chemistry of molecular anions is still highly uncertain.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".