Small Angle X-ray Scattering Simulations for Dark Matter Track Detection
Bibliographic record
Abstract
The existence of dark matter has been demonstrated through observations of galaxy cluster rotations and gravitational lensing, but direct detection searches have been unsuccessful due to technological limitations. Paleodetection has been offered as an alternative indirect method, utilizing ancient minerals and high-resolution imaging to identify crystal defects left behind by dark matter. When an incoming particle hits a nucleus in a crystal, it can knock an atom off its lattice site. The primary knock-on atom then recoils backwards, producing a cascade of collisions that leaves a permanent cylindrical track. As Earth traverses the Milky Way, a large number of these interactions are expected from dark matter and background particles such as neutrinos. The key advantage of paleodetection is that small sample volumes correspond to very long exposure times and thus many potential signal events. Prior work has used transmission electron microscopy and chemical etching to read out tracks, but its pitfall lies in its lackluster efficiency. Small angle X-ray scattering (SAXS), the focus of this work, has been proposed as a solution. A monochromatic beam of X-rays is directed at a sample, scattering off nuclei. In SAXS, the scattering angle is restricted to 0.1–10°, enabling nanometer-scale structural resolution. The difficulty lies in isolating the component of the diffraction pattern caused by dark matter tracks. The goal of this project was to develop numerical simulations of these signals as proof of concept for future experiments. In a perfect crystal, SAXS reveals sharp diffraction peaks. Defects broaden peaks and add noise to flat regions. We simulated Olivine, a candidate mineral, assuming tracks are monodisperse and cylindrical. Track length distributions from prior work were used, and sensitivity thresholds estimated with Asimov datasets while profiling over nuisance parameters. Future work will use Monte Carlo and molecular dynamics to obtain track radius distributions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".