In my study of Dynamic Light Scattering, I came across the following inverse problem. Let $F(s):[0,T]\rightarrow[0,T]$ be the Laplace transform of a probability distribution $f(t)$ on the real line with finite support. This means that $$F(s)=\int_0^{L}e^{-st}f(t)dt$$ for all $s$, while $$\int_0^{L}f(t)dt=1$$ and $f(t)\geq0$ for all $t\in[0,L]$. The numbers $T$ and $L$ are positive real constants. I found that the inverse problem: obtain $f(t)$ from $F(s)$, is ill-posed. Specifically, we can make a sequence of Laplace transforms $F_n(s)$ converge under the $L_2$ norm to some limiting $F(s)$, while the sequence of PDFs $f_n(t)$ does not converge to $f(t)$. I'll give an example at the end of the question.

However, it seems to be different for the cumulative distribution function. Let the CDF be defined by
$$C(t):= \int_0^t f(t')dt'.$$
**The question:**
If the Laplace transforms $F_n(s)$ of $f_n(t)$ converge under the $L_2$ norm to the Laplace transform $F(s)$ of $f(t)$, does it follow that the CDFs $C_n(t)$ converge under the $L_2$ norm to $C(t)$?

**Finding the PDF is ill-posed**: Let $$f_n(t):=\frac{1}{t\sigma\sqrt{2\pi}} e^{-\frac{-(\ln(t)-\mu)^2}{2\sigma^2}}(1+\sin(2\pi n(\ln t-\mu)/\sigma)).$$
This is a lognormal distribution multiplied by a sinusoid.
Here we take $\mu$ such that $e^{\mu}$ lies inside $[0,L]$, and we take $\sigma$ very small.
Now $f_n(t)$ is a probability distribution for all $n$ (up to a very small normalizing factor). The Laplace transforms $F_n(s)$ of $f_n(t)$ converge uniformly on $[0,T]$ to the Laplace transform $F(s)$ of the lognormal distribution:
$$f(t):=\frac{1}{t\sigma\sqrt{2\pi}} e^{-\frac{-(\ln(t)-\mu)^2}{2\sigma^2}}.$$
However, the PDFs $f_n(t)$ do not converge to $f(t)$ under the $L_2$ norm.