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.XD),0ubornB.toUniformBornDataDx)(\forall (x : P.X\,D), 0 \le \mathrm{uborn}\,B.\mathrm{toUniformBornData}\,D\,x)
  2. .sum(ubornB.toUniformBornDataD)=1.\mathrm{sum}\,(\mathrm{uborn}\,B.\mathrm{toUniformBornData}\,D) = 1
  3. (x:P.XD),ubornB.toUniformBornData((A.actg)D)((A.γgD)x)=ubornB.toUniformBornDataDx\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(actSectiongλ)(g.actD)=(g.γD)(selectorλD)\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.toPoincareg)λ)((A.actg)D)=(A.γgD)(selectorλD)\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(diagBooste)(freeFieldMeasureν)=freeFieldMeasureν\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. IsProbabilityMeasureμ\mathrm{IsProbabilityMeasure}\,\mu
  2. (diagNethbhphsumhp1g).toFiniteMarginals.IsLimitμ(\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. IsProbabilityMeasureμ\mathrm{IsProbabilityMeasure}\,\mu
  2. (fockVacuumNetg).toFiniteMarginals.IsLimitμ(\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{seedselects(contWeightsSξs)seedk}=contWeightsSξsk\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.Pxay=S.PAlicexaS.P\,x\,a\,y = S.\mathrm{PAlice}\,x\,a

bipartite_no_signaling

bipartite_no_signaling · spine — we have

b(ρkroneckerMap(λx1x2x1x2)E(Fb)).trace=(ρkroneckerMap(λx1x2x1x2)E1).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