Hunting for dark halo substructure using submilliarcsecond-scale observations of macrolensed radio jets
Bibliographic record
Abstract
Dark halo substructure may reveal itself through secondary, small-scale gravitational lensing effects on light sources that are macrolensed by a foreground galaxy. Here, we explore the prospects of using Very Long Baseline Interferometry (VLBI) observations of multiply-imaged quasar jets to search for submilliarcsecond-scale image distortions produced by various forms of dark substructures in the 103–108 M⊙ mass range. We present lensing simulations relevant for the angular resolutions attainable with the existing European VLBI Network, the global VLBI array and an upcoming observing mode in which the Atacama Large Millimeter Array (ALMA) is connected to the global VLBI array. While observations of this type would not be sensitive to standard cold dark matter subhaloes, they can be used to detect the more compact forms of halo substructure predicted in alternative structure formation scenarios. By mapping approximately five strongly lensed systems, it should be possible to detect or robustly rule out primordial black holes in the 103–106 M⊙ mass range if they constitute ≳1 per cent of the dark matter in these lenses. Ultracompact minihaloes are harder to detect using this technique, but 106–108 M⊙ ultracompact minihaloes could in principle be detected if they constitute ≳10 per cent of the dark matter.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".