Error amplification in solving Prony system with near-colliding nodes

We consider a reconstruction problem for "spike-train" signals $F$ of the a priori known form $ F(x)=\sum_{j=1}^{d}a_{j}\delta\left(x-x_{j}\right),$ from their moments $m_k(F)=\int x^kF(x)dx.$ We assume $m_k(F)$ to be known for $k=0,1,\ldots,2d-1,$ with an absolute error not exceeding $\epsilon > 0$. This problem is essentially equivalent to solving the Prony system $\sum_{j=1}^d a_jx_j^k=m_k(F), \ k=0,1,\ldots,2d-1.$ We study the "geometry of error amplification" in reconstruction of $F$ from $m_k(F),$ in situations where the nodes $x_1,\ldots,x_d$ near-collide, i.e. form a cluster of size $h \ll 1$. We show that in this case error amplification is governed by the "Prony leaves" $S_q{F},$ in the parameter space of signals $F$, along which the first $q+1$ moments remain constant. On this base we produce accurate, up to the constants, lower and upper bounds on the worst case reconstruction error, and show how the Prony leaves can help in improving reconstruction accuracy.