Simultaneous Measurements of Star Formation and Supermassive Black Hole Growth in Galaxies
Bibliographic record
Abstract
Galaxies grow their supermassive black holes in concert with their stars, although the relationship between these major galactic components is poorly understood. Observations of the cosmic growth of stars and black holes in galaxies suffer from disjoint samples and the strong effects of dust attenuation. The thermal infrared holds incredible potential for simultaneously measuring both the star formation and black hole accretion rates in large samples of galaxies covering a wide range of physical conditions. Spitzer demonstrated this potential at low redshift, and by observing some of the most luminous galaxies at z~2. JWST will apply these methods to normal galaxies at these epochs, but will not be able to generate large spectroscopic samples or access the thermal infrared at high-redshift. An order of magnitude gap in our wavelength coverage will persist between JWST and ALMA. A large, cold infrared telescope can fill this gap to determine when (in cosmic time), and where (within the cosmic web), stars and black holes co-evolve, by measuring these processes simultaneously in statistically complete and unbiased samples of galaxies to z>8. A next-generation radio interferometer will have the resolution and sensitivity to measure star-formation and nuclear accretion in even the dustiest galaxies. Together, the thermal infrared and radio can uniquely determine how stars and supermassive blackholes co-evolve in galaxies over cosmic time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".