Past exam of the mathematics course of the University of Cambridge 2007 iii Paper 74 3 d Solution Created 2026-10-03 Updated 2026-10-14
By symmetry the normalized covariance matrix has the form . Substituting the matrices from part (c) into the normalized stationary fluctuation-dissipation relation for a reaction network, , givesHence and . Solving these two equations gives the linear noise of two species with joint removal:For the normalized variance is and the covariance is zero, as expected for independent immigration–death processes. For , exceeds this independent value and . 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 and . Thus the sum mode retains finite fluctuations, with , whereas the difference mode becomes arbitrarily noisy. In particular, at a fixed nonzero mean, or along at fixed so ,The Fano factor of either population is , which diverges as regardless of how the mean is varied. Unnormalized variances are and covariance , so their absolute magnitudes also depend on the parameter path through .
The phase plane in part (b) explains the singularity. At , 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 unchanged, but the two constant-rate births change it by and , each at rate . 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 . The divergence as 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.