Phase transitions and noise sensitivity on the Poisson space via stopping sets and decision trees
Proofs of sharp phase transition and noise sensitivity in percolation have been significantly simplified by the use of randomized algorithms, via the OSSS inequality (proved by O’Donnell, Saks, Schramm and Servedio in ) and the Schramm-Steif inequality for the Fourier-Walsh coefficients of functions defined on the Boolean hypercube (see ). In this article, we prove intrinsic versions of the OSSS and Schramm-Steif inequalities for functionals of a general Poisson process, and apply these new estimates to deduce sufficient conditions — expressed in terms of randomized stopping sets — yielding sharp phase transitions, quantitative noise sensitivity, exceptional times and bounds on critical windows for monotonic Boolean Poisson functions. Our analysis is based on a new general definition of ‘stopping set’, not requiring any topological property for the underlying measurable space, as well as on the new concept of a ‘continuous-time decision tree’, for which we establish several fundamental properties. We apply our findings to the k-percolation of the Poisson Boolean model and to the Poisson-based confetti percolation with bounded random grains. In these two models, we reduce the proof of sharp phase transitions for percolation, and of noise sensitivity for crossing events, to the construction of suitable randomized stopping sets and the computation of one-arm probabilities at criticality. This enables us to settle an open problem suggested by Ahlberg, Tassion and Texeira  (a special case of which was conjectured earlier by Ahlberg, Broman, Griffiths and Morris ) on noise sensitivity of crossing events for the planar Poisson Boolean model with random balls whose radius distribution has finite (2 +α)-moments and also show the same for planar confetti percolation model with bounded random balls. We also prove that critical probability is 1/2 for the planar confetti percolation model with bounded, π/2-rotation invariant and reflection invariant random grains. Such a result was conjectured by Benjamini and Schramm  in the case of fixed balls and proved by M¨uller , Hirsch  and Ghosh and Roy  in the case of balls, boxes and random boxes, respectively; our results contain all previous findings as special cases.
Last, Günter; Peccati, Giovanni; and Yogeshwaran, D, "Phase transitions and noise sensitivity on the Poisson space via stopping sets and decision trees" (2021). ISI Best Publications. 31.