Detailed analysis of multiline molecular distributions in the Seyfert galaxy NGC 1068: Possible effect of the active galactic nucleus outflow to the starburst ring
Bibliographic record
Abstract
Abstract We apply principal component analysis (PCA) to the integrated intensity maps of 13 molecular lines of the nearby type-2 Seyfert galaxy NGC 1068, obtained by the Atacama Large Millimeter/submillimeter Array. We aim to visualize the features of its center objectively, within a radius of about 2 kpc (∼27″̣5; hereafter, the overall region) and within the ring-shaped starburst region between 750 pc (∼10″) and 2 kpc (∼27″̣5) of the galaxy (hereafter, the SB ring region). PCA is a powerful unsupervised machine-learning technique that extracts key information through dimensionality reduction. The PCA results for the overall region have the potential to reconstruct a map representing the approximate H2 column density and the difference of volume density and/or chemical composition between the circumnuclear disk and the starburst ring (SB ring). Additionally, the PCA results for the SB ring region have the potential to reconstruct a map representing the approximate H2 column density and the distinction between the starburst-dominated region and the shock-dominated region. Furthermore, the PCA results for the SB ring region indicate a possible interaction between the active galactic nucleus (AGN) outflow and gas in the SB ring. Although further investigation is required, we suggest that the AGN outflow interacts with gas in the SB ring, as this feature is consistent with the direction of the AGN outflow and is contributed by CN, C2H, and HCN, which are known to be enhanced by the AGN outflow. These results demonstrate that PCA can effectively extract features even for galaxies with complex structures, such as AGN + SB ring. This study also implies that PCA has the potential to uncover previously unrecognized phenomena by visualizing latent structures in multiline data.
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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) | 0.001 | 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".