EFT at FASERν
Bibliographic record
Abstract
Abstract We investigate the sensitivity of the FASERν detector to new physics in the form of non-standard neutrino interactions. FASERν, which will be installed 480 m downstream of the ATLAS interaction point, will for the first time study interactions of multi-TeV neutrinos from a controlled source. Our formalism — which is applicable to any current and future neutrino experiment — is based on the Standard Model Effective Theory (SMEFT) and its counterpart, Weak Effective Field Theory (WEFT), below the electroweak scale. Starting from the WEFT Lagrangian, we compute the coefficients that modify neutrino production in meson decays and detection via deep-inelastic scattering, and we express the new physics effects in terms of modified flavor transition probabilities. For some coupling structures, we find that FASERν will be able to constrain interactions that are two to three orders of magnitude weaker than Standard Model weak interactions, implying that the experiment will be indirectly probing new physics at the multi-TeV scale. In some cases, FASERν constraints will become comparable to existing limits — some of them derived for the first time in this paper — already with 150 fb−1 of data.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.103 | 0.025 |
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".