First WIMP results of XENONnT and its signal reconstruction
Bibliographic record
Abstract
As physicists, we are trying to solve the puzzle that is our Universe. Yet, ordinary, baryonic matter, only accounts for 16% of the total mass of the Universe. The remaining 84% should be some form of matter that has never been observed. This mysterious matter component is called Dark Matter. XENONnT is one of the experiments at the forefront of the search for Dark Matter, and located at a deep underground lab in Italy. It searches, primarily, for a Dark Matter candidate called the weakly interacting massive particle (WIMP) by monitoring a volume of liquid xenon. Other experiments like the Super Cryogenic Dark Matter Search (SuperCDMS) at SNOLAB in Canada, use semiconductors (germanium and silicon) as target materials and may also detect WIMPs, especially if they are relatively light. In this thesis we describe how XENONnT aims to detect WIMPs if they interact with liquid xenon, and how well XENONnT or SuperCDMS may be able to reconstruct the properties of Dark Matter if we find it. In this case, and by combining the results of XENONnT and SuperCDMS, the properties of Dark Matter will be more precisely reconstructed. We further describe the data acquisition and the signal reconstruction of XENONnT. The final chapter of this thesis discusses the latest Dark Matter search result of XENONnT, which sets new stringent limits to the cross-section of the WIMP scattering off ordinary 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".