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By symmetry the normalized covariance matrix has the form $\eta =\left(\begin{array}{cc}V& W\\ W& V\end{array}\right)$. Substituting the matrices from part (c) into the normalized stationary fluctuation-dissipation relation for a reaction network, $M\eta +\eta M^{\mathsf{T}}=D$, givesHence $V+EW=1/\mu$ and $W+EV=E/(2\mu )$. Solving these two equations gives the linear noise of two species with joint removal:For $E=0$ the normalized variance is $1/\mu$ and the covariance is zero, as expected for independent immigration–death processes. For $E>0$, $V=1/\mu +E^2/(2\mu (1-E^2))$ exceeds this independent value and $W<0$. The correlation coefficient isThis negative stationary correlation is compatible with positive instantaneous joint-removal noise: the slow difference mode amplifies fluctuations of opposite signs in the two populations.

The symmetric and antisymmetric eigenvectors diagonalize all three matrices. Their normalized covariance eigenvalues areThe corresponding restoring rates are $(1+E)/\tau =\beta +2C\mu$ and $(1-E)/\tau =\beta$. Thus the sum mode retains finite fluctuations, with ${\eta }_{+}\to 3/(4\mu )$, whereas the difference mode becomes arbitrarily noisy. In particular, at a fixed nonzero mean, or along $\beta \to 0$ at fixed $\lambda ,C>0$ so $\mu \to \sqrt{\lambda /C}$,The Fano factor of either population is $\mu V=(2-E^2)/(2(1-E^2))$, which diverges as $E\to 1$ regardless of how the mean is varied. Unnormalized variances are ${\mu }^{2}V$ and covariance ${\mu }^{2}W$, so their absolute magnitudes also depend on the parameter path through $\mu$.

The phase plane in part (b) explains the singularity. At $\beta =0$, the mean-field system has a whole hyperbola of equilibria, rather than an isolated restoring state. A perturbation in the species difference selects another point on that curve. In the actual stochastic reaction network, joint complex formation still leaves $X_1-X_2$ unchanged, but the two constant-rate births change it by $+1$ and $-1$, each at rate $\lambda$. Therefore exclusive joint removal has a diffusing difference mode:Its continuous-time symmetric simple random walk on the integers has no normalizable stationary distribution, so there is no finite stationary covariance at $E=1$. The divergence as $E\uparrow 1$ signals both the loss of difference-mode restoration and eventual failure of the small-fluctuation moment closure; near the limit, the computed normalized variances need not remain small even when the individual mean populations are large.

Both Poisson processes start at zero and have càdlàg paths. On disjoint time intervals their increments are independent, and independence of the two processes makes the difference increments independent as well. Their increment laws depend only on interval length, so $X$ has stationary increments. For a time interval of length $h$, the probability that either Poisson process has a jump is $1-e^{-h}\to 0$. This proves stochastic continuity. Consequently the difference is a Lévy process, equivalently a continuous-time symmetric simple random walk with total jump rate one and jumps $+1$ and $-1$ each chosen with probability $1/2$.

The characteristic function of a Poisson variable of mean $\lambda$ is $\exp \{\lambda (e^{i\theta }-1)\}$. Independence therefore givesThus, in the negative-exponent convention of part (a),In the positive-exponent convention the answer is $\tilde{\psi }(\theta )=\cos \theta -1$. The Compound Poisson process has mean zero and variance $t$, consistent with the expansion $\psi (\theta )={\theta }^2/2+O({\theta }^4)$.

For the unit-rate continuous-time symmetric simple random walk, the processes $n^{-\alpha }{X}_{nt}$ have three finite-dimensional regimes. For $0<\alpha <1/2$ no probability limit is possible, because the one-time characteristic functions tend to zero off the origin and are discontinuous there. At $\alpha =1/2$ the limit is standard Brownian motion. For $\alpha >1/2$ it is the identically zero Lévy process. This follows from $n(1-\cos (n^{-\alpha }\theta ))\sim ({\theta }^2/2)n^{1-2\alpha }$ and independent increments. Thus a nonzero limit uniquely determines the exponent, whereas merely asking for a Lévy limit does not.