The STUFF between the STARS - On the evolution of the interstellar medium in the real and simulated universe
Bibliographic record
Abstract
In this Thesis, we perform a statistical study of the evolution of the interstellar content of galaxies over cosmic time, using both observational data and results from state-of-the-art cosmological hydrodynamical simulations. \nWe first investigate the evolution of the gas mass fraction for galaxies in the COSMOS field using sub-millimetre emission from dust at 850um. Using stacking methodologies, we derive the gas mass fraction of high-mass galaxies (10^9.5 < M* < 10^11.75) out to high redshift (z<5), including more normal star-forming galaxies than previous studies. We find tentative evidence for a peak in the gas mass fraction of galaxies at around z ~ 2.5-3, just before the peak of the star formation history of the Universe. We find even at high redshifts, high stellar mass galaxies contain significant amounts of gas. \nNext, we use our stacked 850um fluxes to derive galaxy dust masses and develop a post-processing method to estimate dust masses of galaxies from the cosmological hydrodynamical simulation IllustrisTNG. We find that the observations show a strong evolution in the dust content of galaxies with cosmic time - galaxies were dustier in the past. In contrast, the evolution of dust content in the simulated galaxies is comparatively weak. The large discrepancy between observations and simulations plus post-processing may be explained by either strong cosmic evolution in the properties of dust grains, or limitations in the model, possibly connected to a lack of evolution in the neutral gas content of galaxies. \nWe finish with a first-look estimate of the evolution of the comoving dust mass density with cosmic time, by combining stacked dust masses with previously published stellar mass functions of the COSMOS field to generate high-redshift (z<2.5) dust mass functions. In agreement with literature studies, we find a peak in the dust mass density at z ~ 1-1.8.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".