Galaxy formation with local photoionization feedback – I. Methods
Bibliographic record
Abstract
We present a first study of the effect of local photoionizing radiation on gas cooling in smoothed particle hydrodynamics simulations of galaxy formation. We explore the combined effect of ionizing radiation from young and old stellar populations. The method computes the effect of multiple radiative sources using the same tree algorithm as used for gravity, so it is computationally efficient and well resolved. The method foregoes calculating absorption and scattering in favour of a constant escape fraction for young stars to keep the calculation efficient enough to simulate the entire evolution of a galaxy in a cosmological context to the present day. This allows us to quantify the effect of the local photoionization feedback through the whole history of a galaxy's formation. The simulation of a Milky Way-like galaxy using the local photoionization model forms ∼40 per cent less stars than a simulation that only includes a standard uniform background UV field. The local photoionization model decreases star formation by increasing the cooling time of the gas in the halo and increasing the equilibrium temperature of dense gas in the disc. Coupling the local radiation field to gas cooling from the halo provides a preventive feedback mechanism which keeps the central disc light and produces slowly rising rotation curves without resorting to extreme feedback mechanisms. These preliminary results indicate that the effect of local photoionizing sources is significant and should not be ignored in models of galaxy formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".