Kaonic hydrogen X-rays — experiments at DAFNE
Bibliographic record
Abstract
At the DAΦNE electron–positron collider of Laboratori Nazionali di Frascati we study kaonic atoms, taking advantage of the low-energy kaons produced in the Φ-meson decay. The low-energy kaon–nucleon interaction in kaonic hydrogen and kaonic deuterium can be investigated under favorable conditions. The DEAR (DAΦNE Exotic Atom Research) experiment at LNF delivered the most precise data on kaonic hydrogen up to now. DEAR and its follow-up experiment SIDDHARTA (Silicon Drift Detector for Hadronic Atom Research by Timing Application) are using X-ray spectroscopy of kaonic hydrogen and kaonic deuterium atoms to measure the strong interaction-induced shift and width of the ground state. From these quantities the isospin-dependent antikaon–nucleon scattering lengths can be determined, quantities useful for testing the understanding of chiral symmetry breaking in the strangeness sector. Within the SIDDHARTA project new X-ray detectors are being developed. We will use an array of large area silicon drift detectors (SDDs) having excellent energy resolution but also providing a timing capability that will result in a huge suppression of background and so overcome the precision limits of the former experiments.PACS Nos.: 36.10.k, 13.75.Jz, 32.30.Rj and 29.40.Wk
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".