Low surface brightness galaxies in <i>z</i> > 1 galaxy clusters: HST approaching the progenitors of local ultra diffuse galaxies
Bibliographic record
Abstract
Ultra diffuse galaxies (UDGs) are a type of large low surface brightness (LSB) galaxies with particularly large effective radii (reff > 1.5 kpc) that are now routinely studied in the Local (z < 0.1) Universe. While they are found to be abundant in clusters, groups, and in the field, their formation mechanisms remain elusive and comprise an active topic of debate. New insights may be found by studying their counterparts at higher redshifts (z > 1.0), even though cosmological surface brightness dimming makes them particularly difficult to detect and study in this channel. In this work, we use the deepest Hubble Space Telescope (HST) imaging stacks of z > 1 clusters, namely, SPT-CL J2106−5844 and MOO J1014+0038. These two clusters, at z = 1.13 and z = 1.23, respectively, were monitored as part of the HST See-Change programme. In making a comparison with the Hubble Extreme Deep Field as the reference field, we find statistical over-densities of large LSB galaxies in both clusters. Based on stellar-population modelling and assuming no size evolution, we find that the faintest sources we can detect are about as bright as expected for the progenitors of the brightest local UDGs. We find that the LSBs we detect in SPT-CL J2106−5844 and MOO J1014−5844 already have old stellar populations that place them on the red sequence. In correcting for incompleteness and based on an extrapolation of local scaling relations, we estimate that distant UDGs are relatively under-abundant, as compared to local UDGs, by a factor ∼3. A plausible explanation for the implied increase over time would be the significant growth of these galaxies over the last ∼8 Gyr, as also suggested by hydrodynamical simulations.
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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 | 0.001 |
| 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 | 0.000 |
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".