Inferring the mass of submillimetre galaxies by exploiting their gravitational magnification of background galaxies
Bibliographic record
Abstract
Dust emission at submillimetre wavelengths allows us to trace the early phases of star formation in the Universe. In order to understand the physical processes involved in this mode of star formation, it is essential to gain knowledge about the dark matter structures – most importantly their masses – that submillimetre galaxies live in. Here we use the magnification effect of gravitational lensing to determine the average mass and dust content of submillimetre galaxies with 250 μm flux densities of S250 > 15 mJy selected using data from the Herschel Multi-tiered Extragalactic Survey. The positions of hundreds of submillimetre foreground lenses are cross-correlated with the positions of background Lyman-break galaxies at z ∼ 3–5 selected using optical data from the Canada–France–Hawaii Telescope Legacy Survey. We detect a cross-correlation signal at the 7σ level over a sky area of 1 deg2, with ∼80 per cent of this signal being due to magnification, whereas the remaining ∼20 per cent comes from dust extinction. Adopting some simple assumptions for the dark matter and dust profiles and the redshift distribution enables us to estimate the average mass of the haloes hosting the submillimetre galaxies to be log 10[M200/M⊙] = 13.17+ 0.05− 0.08(stat.) and their average dust mass fraction (at radii of >10 kpc) to be Mdust/M200 ≈ 6 × 10−5. This supports the picture that submillimetre galaxies are dusty, forming stars at a high rate, reside in massive group-sized haloes and are a crucial phase in the assembly and evolution of structure in the Universe.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".