Optimizing the Determination of the Neutrino Mixing Angle $\theta_{13}$ from Reactor Data
Amir N. Khan, Douglas W. McKay, and John P. Ralston

TL;DR
This paper argues that simpler, minimal-parameter statistical methods outperform complex multi-parameter approaches in determining the neutrino mixing angle $ heta_{13}$ from reactor data, leading to more reliable and sharper results.
Contribution
It demonstrates that minimal-parameter methods provide more reliable estimates of $ heta_{13}$ and critiques existing multi-parameter approaches used by Daya Bay and RENO.
Findings
Minimal-parameter methods outperform multi-parameter methods.
Existing analyses use non-standard significance measures.
Complex models degrade experimental resolution and increase sensitivity to arbitrary parameters.
Abstract
The technical breakthroughs of multiple detectors developed by Daya Bay and RENO collaborations have gotten great attention. Yet the optimal determination of neutrino mixing parameters from reactor data depends on the statistical method and demands equal attention. We find that a straightforward method using a minimal parameters will generally outperform a multi-parameter method by delivering more reliable values with sharper resolution. We review standard confidence levels and statistical penalties for models using extra parameters, and apply those rules to our analysis. We find that the methods used in recent work of the Daya Bay and RENO collaborations have several undesirable properties. The existing work also uses non-standard measures of significance which we are unable to explain. A central element of the current methods consists of variationally fitting many more parameters than…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
