Bibliographic record
Abstract
This thesis is a study of massive star formation: the environments in which they form and the effect that their radiation feedback has on their environments. We present high-performance supercomputer simulations of massive star formation inside molecular cloud clumps and cores. First, we present a novel radiative transfer code that hybridizes two previous approaches to radiative transfer (raytracing and flux-limited diffusion) and implements it in a Cartesian grid-based code with adaptive mesh refinement, representing the first of such implementations. This hybrid radiative transfer code allows for more accurate calculations of the radiation pressure and irradiated gas temperature that are the hallmark of massive star formation and which threaten to limit the mass which stars can ultimately obtain. Next, we apply this hybrid radiative transfer code in simulations of massive protostellar cores. We simulate their gravitational collapse and the formation of a massive protostar surrounded by a Keplerian accretion disk. These disks become gravitationally unstable, increasing the accretion rate onto the star, but do not fragment to form additional stars. We demonstrate that massive stars accrete material predominantly through their circumstellar disks, and via radiation pressure drive large outflow bubbles that appear stable to classic fluid instabilities. Finally, we present simulations of the larger context of star formation: turbulent, magnetised, filamentary cloud clumps. We study the magnetic field geometry and accretion flows. We find that in clouds where the turbulent and magnetic energies are approximately equal, the gravitational energy must dominate the kinetic energy for there to be a coherent magnetic field structure. Star cluster formation takes place inside the primary filament and the photoionisation feedback from a single massive star drives the creation of a bubble of hot, ionised gas that ultimately engulfs the star cluster and destroys the filament.
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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 | 0.000 |
| 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) | 0.012 | 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".