Detection of substructure with adaptive optics integral field spectroscopy of the gravitational lens B1422+231
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Bibliographic record
Abstract
Strong gravitational lenses can be used to detect low-mass subhaloes, based on deviations in image fluxes and positions from what can be achieved with a smooth mass distribution. So far, this method has been limited by the small number of (radio-loud, microlensing-free) systems which can be analysed for the presence of substructure. Using the gravitational lens B1422+231, we demonstrate that adaptive optics integral field spectroscopy can also be used to detect dark substructures. We analyse data obtained with OH Suppressing Infra-Red Imaging Spectrograph on the Keck i Telescope, using a Bayesian method that accounts for uncertainties relating to the point spread function and image positions in the separate exposures. The narrow-line [O iii] fluxes measured for the lensed images are consistent with those measured in the radio, and show a significant deviation from what would be expected in a smooth mass distribution, consistent with the presence of a perturbing low-mass halo. Detailed lens modelling shows that image fluxes and positions are fitted significantly better when the lens is modelled as a system containing a single perturbing subhalo in addition to the main halo, rather than by the main halo on its own, indicating the significant detection of substructure. The inferred mass of the subhalo depends on the subhalo mass density profile: the 68 per cent confidence intervals for the perturber mass within 600 pc are 8.2|$^{+0.6}_{-0.8}$|, 8.2|$^{+0.6}_{-1}$| and 7.6 ± 0.3 log10[Msub/M⊙], respectively, for a singular isothermal sphere, a pseudo-Jaffe and a Navarro–Frenk–White mass profile. This method can extend the study of flux ratio anomalies to virtually all quadruply imaged quasars, and therefore offers great potential to improve the determination of the subhalo mass function in the near future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it