Causal graph has a higher density of nodes; but our measuring stick is based on the spatial graph
Rest mass: change in density of the non-propagating rate of causal edges
Add edges to the underlying hypergraph
Energy is the number of events per unit time (aka per generation)
Rest mass: change in density of the non-propagating rate of causal edges
Add edges to the underlying hypergraph
Energy is the number of events per unit time (aka per generation)
Rest mass = Number of events per generation
Rest mass = number of causal nodes (aka events) per generation [events have fixed number of causal edges]
Energy/momentum = flux of causal edges
[ Rest mass associated with nodes in the causal graph ]
Edges slice the hypersurfaces in causal graph
Depends on frame whether a piece of “edge flux” has a node involved
Edge of light cone? Edges go further.....
Rest mass = number of causal nodes (aka events) per generation [events have fixed number of causal edges]
Energy/momentum = flux of causal edges
[ Rest mass associated with nodes in the causal graph ]
Edges slice the hypersurfaces in causal graph
Depends on frame whether a piece of “edge flux” has a node involved
Edge of light cone? Edges go further.....
Have to measure energy in the spatial graph ( energy per unit volume )
Say every edge in the causal graph is a quantum of energy
What we see is energy per spatial volume
Say every edge in the causal graph is a quantum of energy
What we see is energy per spatial volume
Assume there are also vertical edges
In[]:=
lorentz[β_][{t_,x_}]:={t-βx,-tβ+x}/Sqrt[1-β^2]
In[]:=
(lorentz[β][{0,1}]-lorentz[β][{0,-1}])/2
Out[]=
-,
β
1-
2
β
1
1-
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β
In[]:=
(lorentz[β][{1,0}]-lorentz[β][{-1,0}])/2
Out[]=
,-
1
1-
2
β
β
1-
2
β
Imagine a background that updates every cycle; imagine other edges that update more rarely.....