The evolution of the dust temperatures of galaxies in the SFR–<i>M</i><sub>∗</sub>plane up to<i>z</i> ~ 2
Bibliographic record
Abstract
We study the evolution of the dust temperature of galaxies in the SFR-M * plane up to z 2 using far-infrared and submillimetre observations from the Herschel Space Observatory taken as part of the PACS Evolutionary Probe (PEP) and Herschel Multi-tiered Extragalactic Survey (HerMES) guaranteed time key programmes. Starting from a sample of galaxies with reliable star-formation rates (SFRs), stellar masses (M * ) and redshift estimates, we grid the SFR-M * parameter space in several redshift ranges and estimate the mean dust temperature (T dust ) of each SFR-M * -z bin. Dust temperatures are inferred using the stacked far-infrared flux densities (100-500 m) of our SFR-M * -z bins. At all redshifts, the dust temperature of galaxies smoothly increases with rest-frame infrared luminosities (L IR ), specific SFRs (SSFR; i.e., SFR/M * ), and distances with respect to the main sequence (MS) of the SFR-M * plane (i.e., log (SSFR) MS = log [SSFR(galaxy)/SSFR MS (M * , z)]). The T dust -SSFR and T dust - log (SSFR) MS correlations are statistically much more significant than the T dust -L IR one. While the slopes of these three correlations are redshiftindependent, their normalisations evolve smoothly from z = 0 and z 2. We convert these results into a recipe to derive T dust from SFR, M * and z, valid out to z 2 and for the stellar mass and SFR range covered by our stacking analysis. The existence of a strong T dust - log (SSFR) MS correlation provides us with several pieces of information on the dust and gas content of galaxies. Firstly, the slope of the T dust - log (SSFR) MS correlation can be explained by the increase in the star-formation efficiency (SFE; SFR/M gas ) with log (SSFR) MS as found locally by molecular gas studies. Secondly, at fixed log (SSFR) MS , the constant dust temperature observed in galaxies probing wide ranges in SFR and M * can be explained by an increase or decrease in the number of star-forming regions with comparable SFE enclosed in them. And thirdly, at high redshift, the normalisation towards hotter dust temperature of the T dust - log (SSFR) MS correlation can be explained by the decrease in the metallicities of galaxies or by the increase in the SFE of MS galaxies. All these results support the hypothesis that the conditions prevailing in the star-forming regions of MS and far-above-MS galaxies are different. MS galaxies have star-forming regions with low SFEs and thus cold dust, while galaxies situated far above the MS seem to be in a starbursting phase characterised by star-forming regions with high SFEs and thus hot dust.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".