Strong lensing of submillimetre galaxies: a tracer of foreground structure?
Bibliographic record
Abstract
The steep source counts and negative K-corrections of bright submillimetre galaxies (SMGs) suggest that a significant fraction of those observed at high flux densities may be gravitationally lensed, and that the lensing objects may often lie at redshifts above 1, where clusters of galaxies are difficult to detect through other means. In this case, follow-up of bright SMGs may be used to identify dense structures along the line-of-sight. Here, we investigate the probability for SMGs to experience strong lensing, using the latest N-body simulations and observed source flux and redshift distributions. We find that almost all high-redshift sources with a flux density above 100 mJy will be lensed if they are not relatively local galaxies. We also give estimates of the fraction of sources experiencing strong lensing as a function of observed flux density. This has implications for planning follow-up observations for bright SMGs discovered in future surveys with the Submillimetre Common-User Bolometer Array 2 and other instruments. The largest uncertainty in these calculations is the maximum allowed lensing amplification, which is dominated by the presently unknown spatial extent of SMGs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".