Galaxies with Biconical Ionized Structure in MaNGA - I. Sample Selection and Driven Mechanisms
Bibliographic record
Abstract
Based on the integral field unit (IFU) data from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we develop a new method to select galaxies with biconical ionized structures, building a sample of 142 edge-on biconical ionized galaxies. We classify these 142 galaxies into 81 star-forming galaxies, 31 composite galaxies, and 30 AGNs (consisting of 23 Seyferts and 7 LI(N)ERs) according to the {\nii}-BPT diagram. The star-forming bicones have bar-like structures while AGN bicones display hourglass structures, and composite bicones exhibit transitional morphologies between them due to both black hole and star-formation activities. Star-forming bicones have intense star-formation activities in their central regions, and the primary driver of biconical structures is the central star formation rate surface density. The lack of difference in the strength of central black hole activities (traced by dust attenuation corrected {\oiii}$λ$5007 luminosity and Eddington ratio) between Seyfert bicones and their control samples can be naturally explained as that the accretion disk and the galactic disk are not necessarily coplanar. Additionally, the biconical galaxies with central LI(N)ER-like line ratios are edge-on disk galaxies that show strong central dust attenuation. The radial gradients of {\ha} surface brightness follow the $r^{-2.35}$ relation, roughly consistent with $r^{-2}$ profile, which is expected in the case of photoionization by a central point-like source. These observations indicate obscured AGNs or AGN echoes as the primary drivers of biconical structures in LI(N)ERs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".