Dust-penetrated morphology in the high-redshift universe: Clues from NGC 922
Bibliographic record
Abstract
Results from the Hubble Deep Field (HDF) North and South show a large percentage of high-redshift galaxies whose appearance falls outside traditional classification systems. The nature of these objects is poorly understood, but sub-mm observations indicate that at least some of these systems are heavily obscured (Sanders [CITE]). This raises the intriguing possibility that a physically meaningful classification system for high-redshift galaxies might be more easily devised at rest-frame infrared wavelengths, rather than in the optical regime. Practical realization of this idea will become possible with the advent of the Next Generation Space Telescope (NGST). In order to explore the capability of NGST for undertaking such science, we present NASA-IRTF and SCUBA observations of NGC 922, a chaotic system in our local Universe which bears a striking resemblance to objects such as HDF 2-86 () in the HDF North. If objects such as NGC 922 are common at high-redshifts, then this galaxy may serve as a local morphological "Rosetta stone"bridging low and high-redshift populations. In this paper we demonstrate that quantitative measures of galactic structure are recoverable in the rest-frame infrared for NGC 922 seen at high redshifts using NGST, by simulating the appearance of this galaxy at redshifts and in rest-frame K'. While this object cannot be classified within any optical Hubble bin, simulated NGST images at these redshifts can be readily classified using the dust penetrated template of Block & Puerari ([CITE]) and Buta & Block ([CITE]). The near-infrared disk of NGC 922 is not peculiar at all; rather, it is remarkably regular, even presenting spiral arm modulation, a characteristic signature of several grand design galaxies. Our results suggest that the capability of efficiently exploring the rest-wavelength IR morphology of high-z galaxies should probably be a key factor in deciding the final choice of instruments for the NGST.
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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 | 0.001 |
| 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 | 0.001 | 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".