Estimating dust temperature and Far-IR luminosity of high-redshift galaxies using ALMA single-band continuum observations
Bibliographic record
Abstract
ABSTRACT We present a method that derives the dust temperatures and infrared (IR) luminosities of high-redshift galaxies assuming radiation equilibrium in a simple dust and stellar distribution geometry. Using public data from the Atacama Large Millimeter/submillimeter Array (ALMA) archive, we studied dust temperatures assuming a clumpy interstellar medium (ISM) model for high-redshift galaxies, then tested the consistency of our results with those obtained using other methods. We find that a dust distribution model assuming a clumpiness of ${\rm log}\, \xi _{\rm clp}=-1.02\pm 0.41$ may accurately represent the ISM of high-redshift star-forming galaxies. By assuming a value of ξclp, our method enables the derivation of dust temperatures and IR luminosities of high-redshift galaxies from dust continuum fluxes and emission sizes obtained from single-band ALMA observations. To demonstrate the method proposed herein, we determined the dust temperature ($T_{\rm d}=95^{+13}_{-17}\, \rm {K}$) of a z ∼ 8.3 star-forming galaxy, MACS0416-Y1. Because the method only requires a single-band dust observation to derive a dust temperature, it is more easily accessible than multiband observations or high-redshift emission line searches and can be applied to large samples of galaxies in future studies using high-resolution interferometers such as ALMA.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".