Show that $f(t)=\sum_{i=1}^n a_i e^{-(x_i-t)^2}-c$ has at most $2n$ zeros Let 
\begin{align}
f(t)=\sum_{i=1}^n a_i e^{-(x_i-t)^2}-c
\end{align} 
where $x_1<x_2<...< x_n$ and $a_i>0$. For some positive constant $c$. 
Can we show that  $f(t)$ has at most $2n$ zeros? 
The intuition here is that if $n=1$ we have a simple bell curve. Then,  a horizontal line $c$ can cross it at most $2$ times. Now if  $n=2 $, then we have two mixed bell curves and we get at most $4$ crossings. 
 A: (This is a follow-up to Gjergji Zaimi's beautiful answer: a minor simplification of the method applied there rather than a different solution. Written as a separate answer upon request of the original poster.)

Let $g_s(x) = (\pi s)^{-1/2} \exp(x^2 / s)$ denote the Gauss–Weierstrass kernel. Note that $g_s * g_t = g_{s + t}$.
Choose $\delta \in (0, 1)$ sufficiently small, so that
$$ \begin{aligned} h(x) & = \sum_{i = 1}^n a_i \delta^{-1/2} \exp((x - x_i)^2 / \delta) - c \\ & = \pi^{1/2} \sum_{i = 1}^n a_i g_\delta(x - x_i) - c \end{aligned} $$
has exactly $2 n$ zeroes; or, more precisely, it changes its sign exactly $2 n$ times. (Is is intuitively clear, and relatively straightforward to prove, that $\delta > 0$ small enough have this property, but the details are a bit messy.) Then $f$ can be written as a convolution of $h$ and $g_{1 - \delta}$:
$$ \begin{aligned} f(x) & = \sum_{i = 1}^n a_i \exp((x - x_i)^2) - c \\ & = \pi^{1/2} \sum_{i = 1}^n a_i g_1(x - x_i) - c \\ & = \biggl(\pi^{1/2} \sum_{i = 1}^n a_i g_\delta(\cdot - x_i) - c\biggr) * g_{1 - \delta}(x) . \end{aligned} $$
However, convolution with the Gauss–Weierstrass kernel does not increase the number of sign-changes: $g_s$ is a Pólya frequency function, and hence a variation diminishing kernel. For more on this, see, for example, review sections written by Samuel Karlin in Selected works of I. J. Schoenberg. It follows that $f$ changes its sign at most $2 n$ times, as claimed.
A: In proposition 7 (together with the proof in the appendix) of Efficiently Learning Mixtures of Two Gaussians, A.T. Kalai, A. Moitra, G. Valiant prove that the linear combination of $n$ Gaussians with distinct variances can have at most $2(n-1)$ zeros. Their argument is inductive adding the Gaussians in order of decreasing variance. Every new Gaussian is added as close to a delta function, which increases the number of zeros by at most 2, and then everything is convolved with a gaussian of appropriate width.
In order to use this in your problem, we have to slightly perturb $f(t)=0$ to
$$e^{-t^2}\left(\sum_{i=1}^n a_i e^{-(1+\epsilon_i)(x_i-t)^2}-c\right)=0$$
for distinct $\epsilon_i$ of small absolute value. This is a linear combination of $n+1$ Gaussians of distinct variances, and therefore has at most $2n$ zeros, as desired.
