Finite-resolution Deconvolution of Multiwavelength Imaging of 20,000 Galaxies in the COSMOS Field: The Evolution of Clumpy Galaxies over Cosmic Time
Bibliographic record
Abstract
Abstract Compact star-forming clumps observed in distant galaxies are often suggested to play a crucial role in galaxy assembly. In this paper, we use a novel approach of applying finite-resolution deconvolution on ground-based images of the COSMOS field to resolve 20,185 star-forming galaxies (SFGs) at 0.5 < z < 2 to an angular resolution of 0.″3 and study their clump fractions. A comparison between the deconvolved images and HST images across four different filters shows good agreement and validates image deconvolution. We model spectral energy distributions using the deconvolved 14-band images to provide resolved surface brightness and stellar-mass density maps for these galaxies. We find that the fraction of clumpy galaxies decreases with increasing stellar masses and with increasing redshift: from ∼30% at z ∼ 0.7 to ∼50% at z ∼ 1.7. Using abundance matching, we also trace the progenitors for galaxies at z ∼ 0.7 and measure the fractional mass contribution of clumps toward their total mass budget. Clumps are observed to have a higher fractional mass contribution toward galaxies at higher redshift: increasing from ∼1% at z ∼ 0.7 to ∼5% at z ∼ 1.7. Finally, the majority of clumpy SFGs have higher specific star formation rates (sSFR) compared to the average SFGs at fixed stellar mass. We discuss the implication of this result for in situ clump formation due to disk instability.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".