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In the uniform-label monotone coupling of Bernoulli percolation, let $I_p$ mean that the origin belongs to an infinite percolation cluster, and let $M=\inf \{p:I_p\}$. The occurrence parameters form an upper interval. Therefore $\{M<p\}={\bigcup }_{r<p,r\in \mathbb{Q}}{I}_r$ and $\theta (p-)=\mathbb{P}(M<p)$. Since $\{M<p\}\subseteq I_p\subseteq \{M\le p\}$, the jump is $\theta (p)-\theta (p-)=\mathbb{P}(I_p\cap \{M=p\})$. Occurrence at the infimum is not presumed.

If an increasing event $A$ depends on $m$ sites or edges, the uniform-label monotone coupling of Bernoulli percolation and the union bound give $0\le {\mathbb{P}}_q(A)-{\mathbb{P}}_p(A)\le m(q-p)$ for $p\le q$. A change of the event requires a coordinate with label in $(p,q]$. For bond percolation from the origin to the boundary of $[-n,n{]}^2$, first-hit graph paths use the $4n(2n+1)$ internal edges, so the same count bounds the parameter sensitivity.

In bond percolation on ${\mathbb{Z}}^d$, edges are independently open with probability $p$ and closed otherwise. An open percolation cluster is a connected component using only open edges. In site percolation it is the graph vertices that are independently declared open. The percolation probability $\theta (p)=P_p(0\leftrightarrow \infty )$ measures the chance that the origin lies in an infinite percolation cluster, and the percolation critical probability is $p_c=\inf \{p:\theta (p)>0\}$. Monotone coupling of Bernoulli percolation by independent uniform distribution makes $\theta$ nondecreasing.

The sharpness of the percolation transition says that below $p_c$ connection probabilities on ${\mathbb{Z}}^d$ decay exponentially with distance, while above $p_c$ the percolation probability is positive. There is almost surely a unique infinite percolation cluster in the supercritical regime. On the square lattice, the Harris-Kesten theorem gives bond threshold $1/2$, and there is no infinite percolation cluster at percolation critical probability. The emergence of connections on arbitrarily large scales is the geometric meaning of the phase transition.

Several observables describe this singular behavior. The percolation susceptibility is $\chi (p)=E_p|C(0)|$ for $p<p_c$. A directional correlation length can be defined through exponential two-point decay, $\xi (p{)}^{-1}=-{\lim }_n{n}^{-1}\log P_p(0\leftrightarrow n{e}_1)$. Percolation critical exponents describe leading powers, conveniently defined logarithmically rather than requiring exact asymptotic amplitudes:The first limit of a sequence approaches from above, the other two from below. At percolation critical probability, percolation two-point connection probability is expected to have leading power $|x{|}^{-(d-2+\eta )}$, and the number per site of percolation clusters of size $s$ has leading power $s^{-\tau }$. This cluster-count probability distribution differs from the size-biased probability distribution seen by choosing a site. Scaling relations for critical exponents such as $\gamma =(2-\eta )\nu$ and $2\beta +\gamma =d\nu$ connect the exponents in their applicable scaling regime; hyperscaling relation is not valid above the upper critical dimension of percolation in ordinary mean-field regimes.

The percolation universality hypothesis predicts that dimension and large-scale symmetry, rather than microscopic graph details, determine these exponents and scaling functions. Thus square-lattice bond and triangular-lattice site models are expected to share the planar graph universality class despite different microscopic definitions. Universality does not mean identical critical probabilities, amplitudes or finite-lattice distributions. Exact values and existence of exponents require model-specific theorems, not merely this prediction.

Two dimensions have an additional structure: conformal maps preserve angles, and critical crossing probabilities are expected to be unchanged under conformal transport of the domain and its marked boundary arcs. A crossing probability then depends only on the conformal shape of that marked quadrilateral. The Cardy boundary crossing formula gives this dependence in terms of its boundary cross ratio. For critical triangular-lattice site percolation, conformal invariance and the resulting exponents are established; examples are $\beta =5/36$, $\gamma =43/18$, $\nu =4/3$, with the rigorous statements in leading-power form. These values are summarized in Smirnov and Werner's original paper. Their universality extension to arbitrary planar lattices should not be presented as the same theorem.

An exploration path separating the two colors, with opposite boundary colors on two arcs, has a domain Markov property of a percolation exploration: conditional on the explored portion, the unexplored colors retain their independent laws with the newly exposed boundary colors. Conformal invariance, this domain Markov property and suitable regularity lead to Schramm–Loewner evolution. In the upper half-plane its chordal equation iswhere $B_t$ is standard Brownian motion and time is normalized by half-plane capacity. Critical percolation corresponds to $\kappa =6$. Thus the complicated discrete interface becomes a conformally natural random curve driven by one real Brownian motion. Its crossing and arm probabilities supply critical exponents and, through scaling relations for critical exponents, near-critical singularities. This makes conformal methods predictive tools for the transition, rather than only descriptions of an interface's appearance.

The Harris theorem for square-lattice bond percolation asserts $\theta (1/2)=0$, and hence $p_c\ge 1/2$. Work in the dual square lattice, declaring a dual bond open precisely when its primal bond is closed. At $p=1/2$ this is again independent bond percolation of density one half.

Around a square centered at the origin place four dual rectangles of dimensions $6r$ by $2r$, one on each side, with overlap squares at the corners. Long crossings of all four must meet in the corner squares and their union contains a dual circuit surrounding the central square. The fixed-aspect crossing bound and the Harris lemma give this dual barrier a probability at least $\eta :=c_3^4>0$, independent of $r$.

Take geometrically increasing integer radii, for example with a factor of eight, and shift by half a lattice spacing to lie on the dual lattice. The four-rectangle annuli then have disjoint bond sets. Their barrier events are independent. An infinite primal open path from the origin would have to avoid every closed dual circuit, so the independent annular barriers for percolation giveLetting $j\to \infty$ provesThe extension to smaller $p$ follows by the monotone coupling of Bernoulli percolation. Translation and a countable union also show that no infinite open cluster exists anywhere at $p=1/2$.

Give each nearest-neighbor edge of the cubic lattice an independent Bernoulli distribution state, open with probability $p$. The resulting product measure is denoted ${\mathbb{P}}_p$. In this bond percolation model, $C(0)$ is the percolation cluster of the origin, andThe uniform-label monotone coupling of Bernoulli percolation shows that ${\theta }_d$ is increasing.

Let $c_n(d)$ count $n$-step self-avoiding walks starting at the origin, with $c_0(d)=1$. Splitting a walk after $n$ steps, and discarding the avoidance constraint between the two pieces, gives $c_{n+m}(d)\le c_n(d)c_m(d)$. The Fekete lemma therefore gives the connective constantThe locally finite graph structure means that an infinite percolation cluster at the origin supplies an open self-avoiding walk of every length. Each specified walk has $n$ distinct edges and is open with probability $p^n$. The union bound givesFor $p\mu (d)<1$ the right side tends to zero. Hence the connective-constant lower bound for percolation is $p_c(d)\ge \mu (d{)}^{-1}$.

For the upper bound first work on the square lattice. A finite open percolation cluster has an outer boundary containing a simple closed graph cycle of dual edges, all crossing closed primal edges. Such a dual bond percolation circuit separates that cluster from infinity. Write $N_{\ell }$ for the number of simple dual circuits of length $\ell$ surrounding the origin. Each circuit meets the positive horizontal ray at distance at most $\ell$: its bounding box contains the origin and its diameter is bounded by its length. Choose such an intersection as an anchor and orient the circuit. Removing its last edge leaves a rooted self-avoiding walk of length $\ell -1$ in the translated square lattice. Consequently, for an absolute constant $K$,The exact constant and this possible overcount do not matter. If $(1-p)\mu (2)<1$, the root test givesA summable circuit count alone need not give a total sum below one. To use its tail correctly, let $B_R=[-R,R{]}^2\cap {\mathbb{Z}}^2$ and condition every edge internal to $B_R$ to be open. This finite event has positive probability. A closed dual circuit surrounding all of $B_R$ crosses no internal edge of $B_R$, and so its closed-edge probability remains $(1-p{)}^{\ell }$ under this conditioning. Its length tends to infinity with $R$. Choose $R$ so large that the union bound for all such circuits is below one. With positive conditional probability, none occurs.

On that event, $C(0)$ contains $B_R$ and cannot be finite: a finite cluster containing $B_R$ would have an enclosing closed dual circuit. Thus ${\theta }_2(p)>0$ whenever $p>1-\mu (2{)}^{-1}$. This is the connective-constant Peierls bound, proved by excluding short circuits through the open-box conditioning. An embedded coordinate plane in the cubic lattice has exactly the same bond percolation law as the square lattice, so $p_c(d)\le p_c(2)$. Together,There are $2d$ choices for the first step of a self-avoiding walk and at most $2d-1$ thereafter, because immediate reversal is forbidden. Hence $c_n(d)\le 2d(2d-1{)}^{n-1}$ and $\mu (d)\le 2d-1$. In particular $\mu (2)\le 3$. Substituting with the correct directions of the inequalities gives

Let $A_n$ be the event that the origin has an open graph path to the boundary of $[-n,n{]}^d\cap {\mathbb{Z}}^d$. The graph path can be stopped on its first boundary visit, so $A_n$ depends on only finitely many sites. Its probability $f_n(p)={\mathbb{P}}_p(A_n)$ is a finite sum of terms $p^a(1-p{)}^b$, and is therefore a continuous function of $p$. Also $A_{n+1}\subseteq A_n$, and the König infinity lemma gives ${\bigcap }_n{A}_n=\{0\leftrightarrow \infty \}$. By continuity from above of a measure,The monotone coupling of Bernoulli percolation described below shows that $\theta$ is nondecreasing. For $p<1$, its right limit of a function $\theta (p+)$ exists and is at least $\theta (p)$. For every fixed $n$,Taking the infimum in $n$ proves $\theta (p+)=\theta (p)$. This proves right continuity of percolation probability throughout $[0,1)$; at $1$ right continuity is understood relative to the parameter domain. The argument does not interchange two uncontrolled limits.

Use the probability space $([0,1{]}^{{\mathbb{Z}}^d},\mathcal{F},\mathbb{P})$ with the product measure of the uniform distribution on $[0,1]$. Its coordinate random variables $U_v$ are independent and identically distributed random variables. Define simultaneously for every parameterFor $r\le s$, the indicator function inequality $X_r(v)\le X_s(v)$ holds pointwise at every graph vertex. For fixed $p$, each coordinate has the Bernoulli distribution with parameter $p$, and the coordinates are independent because they are functions of distinct independent $U_v$. Thus every marginal configuration is exactly the required site percolation model. This is a continuum-indexed family of processes, all on one probability space, and is the standard monotone coupling of Bernoulli percolation.

Work on the common probability space from part (c). For a configuration of all the uniforms, the set $S=\{r\in [0,1]:I_r\text{ occurs}\}$ is an upper interval, possibly with or without its lower endpoint. It is nonempty because $I_1$ occurs, and the coupled onset parameter for origin percolation is $M=\inf S$. For $0<p\le 1$,Indeed, occurrence at some $r<p$ implies $M<p$. Conversely, if $M<p$, the definition of infimum yields occurrence at some $s<p$, and a rational $r$ between $s$ and $p$ also satisfies $I_r$. This also proves measurability of $M$ through its strict sublevel sets.

Choose a rational sequence increasing to $p$. By continuity from below of a measure and the monotone coupling of Bernoulli percolation,Moreover, $\{M<p\}\subseteq I_p\subseteq \{M\le p\}$. Hence $I_p$ is the disjoint union of $\{M<p\}$ and $I_p\cap \{M=p\}$, givingIt is important to retain the intersection with $I_p$: occurrence at the infimum is not automatic. At $p=0$ there is no left limit within the parameter domain, so the displayed left-limit assertion is for $p>0$.

Use the following general result, including its site version: independent site percolation on ${\mathbb{Z}}^d$ has almost surely at most one infinite percolation cluster at each fixed parameter; for every fixed $r>p_c$ it has exactly one almost surely. This is the uniqueness of the infinite percolation cluster, also called the Burton-Keane theorem. For $d\ge 2$, the additional general fact $p_c<1$ ensures a choice of a parameter strictly between $p_c$ and $1$. In dimension one, $p_c=1$ and the asserted interval is empty.

Fix $p\in (p_c,1]$ and choose $q\in (p_c,p)$. Let ${\mathcal{C}}_q$ be the unique infinite open connected component of a graph at $q$. Under the monotone coupling of Bernoulli percolation, it is contained in the unique infinite open connected component of a graph at $p$. On $I_p$, the origin and ${\mathcal{C}}_q$ therefore lie in that same connected component of a graph, and a finite $p$-open graph path connects $0$ to some graph vertex of ${\mathcal{C}}_q$.

For this fixed deterministic $p$, the countable set of uniforms satisfies $U_v\ne p$ at every graph vertex almost surely. Thus all uniforms on that finite graph path are strictly less than $p$, including its endpoints. If $t$ is their maximum, choose $r$ with $\max (q,t)<r<p$. The graph path is then $r$-open, and its endpoint remains connected to infinity through ${\mathcal{C}}_q$. Consequently $I_r$ occurs and $M<p$. We have provedPart (d) gives left continuity at this $p$; part (b) supplies right continuity when $p<1$. The same finite-path proof gives left continuity at $p=1$, since all uniforms are strictly less than $1$ almost surely. Thus $\theta$ is continuous on $(p_c,1]$, without asserting continuity at $p_c$. This is supercritical continuity of percolation probability.

First obtain a polynomial critical one-arm upper bound from part (a). Fix a sufficiently large integer radius $r$. Use rotated-coordinate boxes $B_r=[-r,r{]}^2$ for this geometric construction, in the axes of part (a). Surround $B_r$ by a square ring inside $B_{3r}$. It can be assembled from the four stripsThe first two require horizontal dual crossings; the last two require vertical dual crossings. Their corner overlaps are squares, so the adjacent long crossings intersect there and their union contains a dual graph cycle surrounding the inner box. Translate the strips by the half-lattice displacement of the planar dual graph and adjust the endpoints by one lattice unit if needed. All aspect ratios remain bounded. Part (a) and the same overlap gluing give a uniform lower bound $c_1>0$ for each dual crossing at $1/2$. Applying the Harris-FKG inequality to these decreasing events in the primal configuration gives a closed dual circuit with probability at least $\delta =c_1^4>0$. We may replace $\delta$ by a smaller number in $(0,1)$.

Use the independent annular barriers for percolation at radii $r_j=r_{0}10^j$. Their supporting edge sets are disjoint, so the circuit events are independent. In the original unrotated coordinates, $B_{3r}$ lies inside ${\Lambda }_{5r}$ and the ring still separates the origin from infinity. These fixed geometric comparisons let us bound the original $g_N$. An open graph path from $0$ to distance $N$ must avoid every such closed dual circuit inside ${\Lambda }_N$. Thus, for some constants $C<\infty$ and $\alpha >0$,where the first bound is used only when at least one of the indicated rings fits; increasing $C$ handles smaller $N$.

Now let $\epsilon =p-1/2>0$ and use the monotone coupling of Bernoulli percolation on edges. The event $\{0\leftrightarrow \partial {\Lambda }_N\}$ only needs edges whose endpoints lie in ${\Lambda }_N$, because a first boundary hit gives an internal graph path. There are exactly $4N(2N+1)$ such edges. Each changes state between parameters $1/2$ and $p$ with probability $\epsilon$. The union bound therefore gives the finite-box comparison for percolation parametersFor sufficiently small $\epsilon$, choose $N=\lfloor {\epsilon }^{-1/(\alpha +2)}\rfloor$, so that $N$ is at least half the unrounded value. Both terms then have order ${\epsilon }^{\alpha /(\alpha +2)}$. In particular,for a finite $A$. Increase $A$ to cover the remaining compact range of parameters by $\theta (p)\le 1$. This near-critical percolation power upper bound requires only a positive crossing constant, not the exact value of a critical exponent. It also implies $\theta (1/2)=0$ through the critical one-arm probability bound.

Use the nearest-neighbour lattice graph with graph vertex set ${\mathbb{Z}}^d$: an unordered pair $\{x,y\}$ is an edge precisely when $\Vert x-y{\Vert }_1=1$. A bond configuration is $\omega \in \{0,1{\}}^{E_d}$; under the product measure ${\mathbb{P}}_p$, its edge coordinates are independent Bernoulli distribution with success probability $p$. Edges with coordinate one are open. The open cluster $C(0)$ is the connected component of a graph reachable from the origin through open edges.

Define the percolation probability and percolation critical probability byEquivalently, $p_c(d)=\sup \{p:{\theta }_d(p)=0\}$. The monotone coupling of Bernoulli percolation proves the equivalence: assign independent uniform distribution $U_e$ to the edges and open $e$ at parameter $p$ when $U_e\le p$. Connection events and ${\theta }_d$ then increase with $p$. For $d\ge 1$, ${\theta }_d(0)=0$ and ${\theta }_d(1)=1$, so the defining set is nonempty. The definition does not settle what happens at $p=p_c(d)$.

By translation invariance all roots have the same probability. Since the graph vertex set is countable, ${\theta }_d(p)=0$ implies that no graph vertex lies in an infinite percolation cluster almost surely. Conversely a positive root probability gives positive probability of an infinite percolation cluster, and translation ergodicity of Bernoulli percolation then makes existence an almost-sure event. In question 2(b), $\theta$ means ${\theta }_2$.

For every $v\in {\mathbb{Z}}^3$, sample independent $U_v\sim \mathrm{Unif}[0,1]$ and set ${\eta }_p(v)=1_{\{U_v\le p\}}$. Then each ${\eta }_p$ has the required Bernoulli product law and ${\eta }_p\le {\eta }_{p^{\prime }}$ whenever $p\le p^{\prime }$. This is the monotone coupling of Bernoulli percolation.

For the rooted infinite model defining percolation probability, the percolation critical probability isBelow $p_c$, the root has probability zero of belonging to an infinite percolation cluster. Above $p_c$, that probability is positive, by the definition and the monotone coupling of Bernoulli percolation. The definition alone does not determine behavior at $p_c$, or the numerical value of $p_c$: these require results specific to the graph and the model.

A finite graph with $M$ edges has $2^M$ open/closed configurations. The probability of any connectivity event is a finite sum of terms $p^k(1-p{)}^{M-k}$, hence a polynomial in $p$. The square's $2p^2-p^4$ is an example. These finite probabilities vary smoothly, although an infinite-system limit can distinguish a whole interval with $\theta (p)=0$ from one with $\theta (p)>0$. A steep finite connection curve and a mathematically proved percolation critical probability are different kinds of information.

For independent bond percolation on an infinite graph with a specified root $o$, let $C_o$ be its percolation cluster. The percolation probability isIt concerns the root, rather than the existence of an infinite percolation cluster somewhere else. Even above the percolation critical probability of the square lattice, an isolated root has probability $(1-p{)}^4$, because its four incident edges can all be closed. Thus $\theta (p)\le 1-(1-p{)}^4<1$ when $p<1$. Positive percolation probability does not mean every vertex is connected to infinity.

On the square lattice, the events that $o$ is joined by an open path to the boundary of $[-n,n{]}^2$ decrease as $n$ increases. Their probabilities converge to $\theta (p)$ by continuity from above of a measure. This relates an infinite question to finite observations, without equating any one finite boundary connection with survival forever. The monotone coupling of Bernoulli percolation shows that $\theta$ is a monotone function of $p$.

For independent site percolation or bond percolation on a rooted locally finite graph, $\theta (p)$ is right continuous on $[0,1)$. Finite-radius connection events have polynomial probabilities in $p$ and decrease to infinite connection. Thus $\theta$ is their infimum by continuity from above of a measure. This infimum of continuous functions is upper semicontinuous; parameter monotonicity from the monotone coupling of Bernoulli percolation then gives right continuity.

Independent site percolation and bond percolation on the cubic lattice have continuous percolation probability on $(p_c,1]$. Fix $p>p_c$ and an intermediate $q\in (p_c,p)$. By uniqueness of the infinite percolation cluster, every origin in the infinite $p$-cluster has a finite $p$-open graph path to the infinite $q$-cluster. In the uniform-label monotone coupling of Bernoulli percolation, the finitely many labels on this graph path are strictly below fixed $p$ almost surely, so it persists at some parameter below $p$. The coupled onset parameter for origin percolation has no attained atom there, giving left continuity, including at $1$. Combine this with right continuity of percolation probability.