Disc asymmetry characterization in <i>JWST</i>-observed galaxies at 1 &lt; <i>z</i> &lt; 4
Bibliographic record
Abstract
ABSTRACT We present a novel technique using Fourier series and Laguerre polynomials to represent morphological features of disc galaxies. To demonstrate the utility of this technique, we study the evolution of disc asymmetry in a sample of disc galaxies drawn from the Extended Groth Strip and imaged by the JWST Cosmic Evolution Early Release Science Survey as well as archival Hubble Space Telescope observations. We measure disc asymmetry as the amplitude of the $m = 1$ Fourier harmonic for galaxies within redshift ranges of $1 < z < 4$ relative to the amplitude of $m = 0$ harmonic. We show that when viewed in shorter rest-frame wavelengths, disc galaxies have a higher asymmetry as the flux is dominated by star-forming regions. We find generally low asymmetry at rest-frame infrared wavelengths, where our metric tracks asymmetry in morphological features such as bars and spiral arms. We show that higher mass galaxies have lower asymmetry and vice versa. Higher asymmetry in lower mass galaxies comes from lower mass galaxies (typically) having higher star formation rates. We measure the relation between disc galaxy asymmetry and redshift and find no conclusive relationship between them. We demonstrate the utility of the Fourier–Laguerre technique for recovering physically informative asymmetry measurements as compared to rotational asymmetry measurements. We also release the software pipeline and quantitative analysis for each galaxy.
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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 | 0.003 | 0.001 |
| 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) | 0.002 | 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".