Diagnosing the ISM of galaxies and energetic processes in a cosmological context
Bibliographic record
Abstract
The evolution and formation of galaxies is one of the most interesting topics of research in astronomy. In this thesis, I focus on the interstellar medium (ISM) of galaxies and its energetic processes in a cosmological context. First, I dig into the ISM and its phases, which are the main point of interaction between the different gas components within a galaxy. Second, I look at the effects of intense star formation activity and the active galactic nuclei (AGN), which are crucial in the evolution of galaxies. I diagnose the ISM of galaxies using far-infrared (FIR) emission lines, which trace the cooling and heating of gas, to disentangle the ISM phases and analyse their dependence on other properties of the galaxies. Using cosmological simulations I reproduce the expected emissions from FIR lines at different cosmic times and estimate how the ISM phases change during the formation and evolution of galaxies. In addition, I examine the relationship between star-formation rates (SFR) and fractional AGN contributions in galaxies with different activity types to understand their importance in other observed physical parameters. Both SFR and AGN fractional contributions cause large differences in the ISM within a galaxy which are important for the accretion of mass into its supermassive black hole. The results of this thesis show that it is possible to use computational tools to understand the evolution of gas processes at different cosmic times and to estimate physical parameters that help to classify galaxies due to their energetic activity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".