Quasar Negative Feedback to Surrounding Galaxies Probed with Ly$α$ Emitters and Continuum-Selected Galaxies
Bibliographic record
Abstract
We report on the statistical analysis of quasar photoevaporation at $z\sim2.2$ by comparing the density of surrounding Ly$α$ Emitters (LAEs) and continuum-selected galaxies, based on the imaging data of Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) and CFHT Large Area $U$-band Deep Survey (CLAUDS). We select 18 quasars from Sloan Digital Sky Survey (SDSS) in the HSC Deep/UltraDeep fields, normalize the LAE/continuum-selected galaxy distribution around each quasar with the quasar proximity size, stack them, and then measure the average densities of the galaxies. As a result, we find that the density of LAEs is $\gtrsim 5 σ$ lower than that of continuum-selected galaxies within the quasar proximity region. Within the quasar proximity region, we find that the LAEs with high Ly$α$ equivalent widths (EWs) are less dense than those with low EWs at the 3$σ$ level and that LAEs with EW of $\gtrsim150$ Å(rest-frame) are predominantly scarce. Finally, we find that both LAEs and continuum-selected galaxies have smaller densities when they are closer to quasars. We argue that the photoevaporation effect is more effective for smaller dark matter haloes predominantly hosting LAEs, but that it may also affect larger haloes.
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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 | 0.001 | 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) | 0.002 | 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".