Towards T2K neutrino flux predictions using the replica target measurements by NA61/SHINE
Bibliographic record
Abstract
The precise knowledge of the neutrino flux composition and kinematics is one of the biggest challenges of long-baseline experiments such as T2K. Neutrinos are made by the in-flight decay of unstable hadrons produced by the interactions of 31GeV /c protons in a long graphite target. Mostly π + ( π − ) are created, leading to the enhanced flux. As kaons and muons are also produced, an irreducible background of electron (anti-)neutrino is also present. The main source of uncertainty in the flux prediction is driven by the lack of data on the proton-carbon interaction in this energy range. The measurements performed by the NA61/SHINE large-acceptance experiment at CERN are used by the T2K collaboration to improve the flux predictions. Two datasets have been taken: using a thin target to study the primary interaction, and a replica of the T2K target to account for the re-interactions. The recently released differential multiplicity distributions of π ± along the replica target measured in NA61/SHINE will be presented. This dataset is now in the process of being used by T2K to further tune the flux prediction as 90% of the neutrinos will be directly constrained.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".