How do you determine the slope and intercept of a linear trend by Gaussian distribution

I used a desgin matrix approach to determine a linear trend to data that has background noise.

I need to find the slope and intercept of the Gaussian distribution

This is what I have done and I am not sure exactly how to interpret the results

code:

%Build design matrix
X=(2*pi*(60)*t);
A = [ones(length(t),1) exp(-((t-0.0001)/50).^2) sin(X)];
 
%SVD of the design matrix
[U,S,V]= svd(A,0);
W=diag(1./diag(S));
a= V*W*U'*y;
[n, m]= size(A);
redchisqu= sum((A*a-y).^2)/(n-m);
errmat= redchisqu*V*W.^2*V';
 
% display results:
disp(a)
sa = sqrt(diag(errmat));
disp(sa)
 
gaus_fit = a(1) + a(2)*exp(-(t-0.0001).^2/50.^2) + a(3)*sin(X);
 
figure
scatter(t,y,'o','filled')
set(gca,'FontSize',14,'box','on')
xlabel('Time [sec]')
ylabel('Volatile Compound Concentration Signal [arbitrary units]')
hold on
plot(t,gaus_fit,'Linewidth',3)
legend('Signal','Gaussian Peak')