Observing the host galaxies of high-redshift quasars with <i>JWST</i>: predictions from the <scp>BlueTides</scp> simulation
Bibliographic record
Abstract
ABSTRACT The bright emission from high-redshift quasars completely conceals their host galaxies in the rest-frame ultraviolet/optical, with detection of the hosts in these wavelengths eluding even the Hubble Space Telescope (HST) using detailed point spread function (PSF) modelling techniques. In this study, we produce mock images of a sample of z = 7 quasars extracted from the BlueTides simulation, and apply Markov chain Monte Carlo-based PSF modelling to determine the detectability of their host galaxies with the James Webb Space Telescope (JWST). While no statistically significant detections are made with HST, we predict that at the same wavelengths and exposure times JWST NIRCam imaging will detect $\sim 50{{\ \rm per\ cent}}$ of quasar host galaxies. We investigate various observational strategies, and find that NIRCam wide-band imaging in the long-wavelength filters results in the highest fraction of successful quasar host detections, detecting $\gtrsim 80{{\ \rm per\ cent}}$ of the hosts of bright quasars in exposure times of 5 ks. Exposure times of ≳ 5 ks are required to detect the majority of host galaxies in the NIRCam wide-band filters, however, even 10 ks exposures with MIRI result in $\lesssim 30{{\ \rm per\ cent}}$ successful host detections. We find no significant trends between galaxy properties and their detectability. The PSF modelling can accurately recover the host magnitudes, radii, and spatial distribution of the larger scale emission, when accounting for the central core being contaminated by residual quasar flux. Care should be made when interpreting the host properties measured using PSF modelling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".