Probing infrared eXcess to investigate early-Universe dust (PIXIEDust)
Bibliographic record
Abstract
ABSTRACT Despite the implied presence of dust through reddened UV emission in high-redshift galaxies, no dust emission has been detected in the (sub)millimetre regime beyond $z > 8.3$. This study combines around 200 h of Atacama Large Millimetre/submillimetre Array (ALMA) and Northern Extended Millimetre Array (NOEMA) observations on 10 $z > 8$ galaxies, revealing no significant dust emission down to a $1 \sigma$ depth of 2.0, 2.0, and $1.5 \, \mu$Jy at rest-frame 158, 88 $\mu$m, and across all the data, respectively. This constrains average dust masses to be below $< 10^{5}$ M$_{\odot }$ at $3 \sigma$ and dust-to-stellar mass ratios to be below $3.7 \times {} 10^{-4}$ (assuming $T_{\rm dust} = 50$ K and $\beta _{\rm dust} = 2.0$). Binning by redshift ($8 < z < 9.5$ and $9.5 < z < 15$), UV-continuum slope ($\beta _{\rm UV} \lessgtr -2$), and stellar mass ($\log _{10} M_{\ast }/{\rm {M}_{\odot }} \lessgtr 9$) yields similarly stringent constraints. Combined with other studies, these results are consistent with inefficient dust build-up in the $z > 8$ Universe, likely due to inefficient supernova production, limited interstellar grain growth and/or ejection by outflows. We provide data and tools online to facilitate community-wide high-redshift dust searches.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".