Fit of the three E lines: ,
ProvedMethanolMuDrift.eLine_fitFit a straight line, by weighted least squares with weights , to the positions of the three E-symmetry methanol lines of Table 1 plotted against their sensitivity coefficients:
Let be the fitted slope, its standard error and the reduced chi-squared (one degree of freedom). Then the derived estimate , its statistical error and reproduce the paper's values to the stated precision:
This is the paper's fiducial measurement with .
Formalization Note The paper reports rounded values computed from unrounded data; recomputation from the rounded Table 1 entries gives , so the tolerance on the error is rather than a half unit of the last digit. Tolerances on the estimate and on are a half unit of the last reported digit.
import Mathlib import Definitions.Def_Bagdonaite2013_MethanolMuDrift
namespace MethanolMuDrift
theorem eLine_fit :
|muDriftEstimate eK eV eSigma - (-0.1e-8)| ≤ 0.05e-8 ∧
|muDriftStatErr eK eSigma - 7.6e-8| ≤ 0.1e-8 ∧
|wlsReducedChiSq eK eV eSigma - 2.0| ≤ 0.05 := by sorry
end MethanolMuDrift
Read-back
What the Lean code literally says, in plain math · Aristotle (Harmonic) — same agent as the drafter; non-blind
Non-blind read-back — not independent testimony. This read-back was written by the same agent (Aristotle, by Harmonic) that drafted the Lean statement, at the explicit request of the account owner. The author knew the source paper and the intended meaning while writing it, so it is not a blind audit and must not be mistaken for independent testimony. Reviewers should compare the Lean code against the source themselves (or obtain an independent blind read-back).
With the data of the definition file — , , (three points) — let , and be the weighted least-squares slope, slope standard error and reduced chi-squared with weights , and . The statement asserts all three inequalities
It has no hypotheses; it is a closed statement about specific numbers.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.