Target 2 — Lorentz covariance of the selection

Lorentz covariance

← all targets · ← Target 1

Target 2 — Lorentz covariance of the selection

upvm_covariant_probability

upvm_covariant_probability · capstone — we have all of:

  1. (∀(x:P.X D),0≤uborn B.toUniformBornData D x)(\forall (x : P.X\,D), 0 \le \mathrm{uborn}\,B.\mathrm{toUniformBornData}\,D\,x)
  2. .sum (uborn B.toUniformBornData D)=1.\mathrm{sum}\,(\mathrm{uborn}\,B.\mathrm{toUniformBornData}\,D) = 1
  3. ∀(x:P.X D),uborn B.toUniformBornData ((A.act g) D) ((A.γ g D) x)=uborn B.toUniformBornData D x\forall (x : P.X\,D), \mathrm{uborn}\,B.\mathrm{toUniformBornData}\,((A.\mathrm{act}\,g)\,D)\,((A.\gamma\,g\,D)\,x) = \mathrm{uborn}\,B.\mathrm{toUniformBornData}\,D\,x

evaluation_covariance

evaluation_covariance · spine — we have

selector (actSection g λ) (g.act D)=(g.γ D) (selector λ D)\mathrm{selector}\,(\mathrm{actSection}\,g\,\lambda)\,(g.\mathrm{act}\,D) = (g.\gamma\,D)\,(\mathrm{selector}\,\lambda\,D)

group_evaluation_covariance

group_evaluation_covariance · spine — we have

selector (actSection (A.toPoincare g) λ) ((A.act g) D)=(A.γ g D) (selector λ D)\mathrm{selector}\,(\mathrm{actSection}\,(A.\mathrm{toPoincare}\,g)\,\lambda)\,((A.\mathrm{act}\,g)\,D) = (A.\gamma\,g\,D)\,(\mathrm{selector}\,\lambda\,D)

freeFieldMeasure_boost_invariant

freeFieldMeasure_boost_invariant · spine — we have

map (diagBoost e) (freeFieldMeasure ν)=freeFieldMeasure ν\mathrm{map}\,(\mathrm{diagBoost}\,e)\,(\mathrm{freeFieldMeasure}\,\nu) = \mathrm{freeFieldMeasure}\,\nu

assuming

plus 1 routine conditions (1 typeclass) — full list in the per-track PDF.

bh_typicalityMeasure_exists

bh_typicalityMeasure_exists · spine — there is μ\mu such that all of:

  1. IsProbabilityMeasure μ\mathrm{IsProbabilityMeasure}\,\mu
  2. (diagNet hb hp hsum hp1 g).toFiniteMarginals.IsLimit μ(\mathrm{diagNet}\,\mathrm{hb}\,\mathrm{hp}\,\mathrm{hsum}\,\mathrm{hp1}\,g).\mathrm{toFiniteMarginals}.\mathrm{IsLimit}\,\mu

assuming

plus 4 routine conditions (4 typeclass) — full list in the per-track PDF.

fock_typicalityMeasure_exists

fock_typicalityMeasure_exists · spine — there is μ\mu such that all of:

  1. IsProbabilityMeasure μ\mathrm{IsProbabilityMeasure}\,\mu
  2. (fockVacuumNet g).toFiniteMarginals.IsLimit μ(\mathrm{fockVacuumNet}\,g).\mathrm{toFiniteMarginals}.\mathrm{IsLimit}\,\mu

plus 3 routine conditions (3 typeclass) — full list in the per-track PDF.

continuum_volume_selects

continuum_volume_selects · spine — we have

vol {seed∣selects (contWeights S ξ s) seed k}=contWeights S ξ s k\mathrm{vol}\,\{\mathrm{seed}|\mathrm{selects}\,(\mathrm{contWeights}\,S\,\xi\,s)\,\mathrm{seed}\,k\} = {{\mathrm{contWeights}\,S\,\xi\,s\,k}}

plus 1 routine conditions (1 typeclass) — full list in the per-track PDF.

no_signaling

no_signaling · spine — we have

S.P x a y=S.PAlice x aS.P\,x\,a\,y = S.\mathrm{PAlice}\,x\,a

bipartite_no_signaling

bipartite_no_signaling · spine — we have

∑b(ρ⋅kroneckerMap (λx1x2↦x1⋅x2) E (F b)).trace=(ρ⋅kroneckerMap (λx1x2↦x1⋅x2) E 1).trace\sum_{b} (\rho \cdot \mathrm{kroneckerMap}\,(\lambda x_{1} x_{2} \mapsto x_{1} \cdot x_{2})\,E\,(F\,b)).\mathrm{trace} = (\rho \cdot \mathrm{kroneckerMap}\,(\lambda x_{1} x_{2} \mapsto x_{1} \cdot x_{2})\,E\,1).\mathrm{trace}

assuming

no_covariant_selector

no_covariant_selector · nogo — we have

⊥\bot

assuming

bool_swap_no_selector

bool_swap_no_selector · nogo — we have

⊥\bot

assuming