Absence of renormalization group pathologies near the critical temperature. Two examples

We consider real-space renormalization group transformations for Ising-type systems which are formally defined by $$\exp \left[ { - H'(\sigma ')} \right] = \sum\limits_\sigma {T(\sigma ,\sigma ')} \exp \left[ { - H(\sigma )} \right]$$ whereT(σ, σ′) is a probability kernel, i.e., ∑_σ′ T(σ,σ′) = 1 for every configuration σ. For each choice of the block spin configuration σ′, let σ′, let μ_σ′ be the measure on spin configurations σ which is formally given by taking the probability of σ to be proportional toT(σ, σ′) exp[?H(σ)]. We give a condition which is sufficient to imply that the renormalized HamiltonianH′ is defined. Roughly speaking, the condition is that the collection of measures μ_σ′ is in the high-temperature phase uniformly in the block spin configuration σ′. The proof of this result uses methods of Olivieri and Picco. We use our theorem to prove that the first iteration of the renormalization group transformation is defined in the following two examples: decimation with spacingb = 2 on the square lattice with β < 1.36β _c and the Kadanoff transformation with parameterp on the trian gular lattice in a subset of the β,p plane that includes values of β greater than β _c .     

Diffractive production of ρ-mesons and of ρπ-systems by neutrinos and antineutrinos on protons

Evidence is presented for diffractive production of -mesons and of -systems invp and chargedcurrent interactions. In the (anti-)neutrino energy range 10 GeVE _v <60 gev=" the=" cross=" sections=" for=" diffractive="> and diffractive production are found to be (0.64±0.14 (stat.)±0.08 (syst.))% and (0.28±0.08 (stat.)±0.04 (syst.))% of the charged-current cross section. The diffractive signal is consistent with being entirely due to diffractivea ₁ production. However, the data cannot distinguish between diffractivea ₁ and diffractive nonresonant production. The experimental distributions ofW, Q ²,x _Bj andy _Bj for diffractive and events are consistent with model predictions.