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

lorentz[β_][{t_,x_}]:={t-βx,-tβ+x}/Sqrt[1-β^2]

In[]:=

(lorentz[β][{0,1}]-lorentz[β][{0,-1}])/2

In[]:=

-,

β

1-

2

β

1

1-

2

β

Out[]=

(lorentz[β][{1,0}]-lorentz[β][{-1,0}])/2

In[]:=

,-

1

1-

2

β

β

1-

2

β

Out[]=

Imagine a background that updates every cycle; imagine other edges that update more rarely.....