# Normalization and boundary issues with numerical convolution (ListConvolve function)

I have a somewhat messy piece-wise function that I need to convolve with a Gaussian function. Solving the problem analytically is taking forever so I would like to solve the problem numerically. However, I am having a few issues and any help would be greatly appreciated.

Here is an example of simplified version of what I am trying to implement using ListConvolve.

Following is a convolution between a simple trig function and Gaussian function done analytically.

response[t_] := 1/Sqrt[2 Pi  .2^2)] Exp[-(t^2/(2* .2^2))]
func[t_] := (Sin[3*t])^2
val = Integrate[response[t-Tau]*func[t], {t, -10, 10}];
Plot[{func[Tau], val}, {Tau, -5, 5}, PlotLegends -> {"Original", "Convolved"}]


The result is what I expected. Now I would like to do the same problem numerically (i.e. find convolution). I tried using ListConvolve, a built in function

unconvfunc = Table[{t, func[t]}, {t, -10, 10, 0.01}];
resdata =  Table[{t, response[t]/100}, {t, -2, 2, 0.01}];
convfunc = ListConvolve[resdata[[All, 2]], unconvfunc[[All, 2]], 1];
ListLinePlot[{unconvfunc, Transpose[{unconvfunc[[All, 1]], convfunc}]},
PlotRange -> {{-5, 5}, {0, 1}}, PlotLegends -> {"Original", "Convolved"}]


I run into two problems. First the time axis is shifted for the convolved data. In the specific example if I pick the argument for ListConvolve[#,#,97] to be 97 instead of 1, the convolved plot seems to be more or less aligned with my original data. Also to get the normalization of the convfunc properly I divide the resdata by 1/stepsize. How would I pick those two parameters intelligently such that the numerical convolution would agree with the analytical one.

Thank you