Equation 27 · The Bend an Elevator Cannot Fake
What does this equation mean?
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This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Symbol hatphi
the standard weighted least-squares estimate of the node potentials.
Symbol B^top
op is one of the signed contributions combined to compute the quantity on the left.
Symbol C^-1
1 is one of the signed contributions combined to compute the quantity on the left.
Symbol B
B is one of the signed contributions combined to compute the quantity on the left.
Symbol y
y is one of the signed contributions combined to compute the quantity on the left.
=
The expressions on both sides represent the same quantity under the stated assumptions.
See an illustrated explanation →superscript
A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.
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What the article says around this equation
At weak field this reduces to the familiar ( - )/ for Newtonian potential , exactly the quantity real fibre-linked clock comparisons already report [ 1 , 7 ] . Collect every measured edge into a vector y , one entry per link, and encode the network’s topology in an incidence matrix B : row e = (i,j) has a +1 in column i , a -1 in column j , and zero elsewhere. The model is y = B + n for measurement noise n with covariance C , and the standard weighted least-squares estimate of the node potentials is . using the Moore–Penrose pseudoinverse because B has a nontrivial null space: adding any constant to every node’s leaves every edge…
Read the full surrounding passage
At weak field this reduces to the familiar ( - )/ for Newtonian potential , exactly the quantity real fibre-linked clock comparisons already report [ 1 , 7 ] . Collect every measured edge into a vector y , one entry per link, and encode the network’s topology in an incidence matrix B : row e = (i,j) has a +1 in column i , a -1 in column j , and zero elsewhere. The model is y = B + n for measurement noise n with covariance C , and the standard weighted least-squares estimate of the node potentials is . using the Moore–Penrose pseudoinverse because B has a nontrivial null space: adding any constant to every node’s leaves every edge difference, and hence every prediction, unchanged. This is not a defect to patch. It is the precise algebraic statement that a network can only ever certify potential differences, never an absolute zero, matching the physical fact that gravitational redshift has never been measured as anything but a difference [ 4 , 3 ] . Anchoring any one node’s height to an external, independently surveyed vertical datum removes the remaining gauge freedom without adding a single new physical assumption to the network itself.
For background, read the article’s source list.
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