Why Is AIC computed with a term containing -log(SSR) instead of -SSR? Looking at https://en.wikipedia.org/wiki/Akaike_information_criterion I find the well known log likelihood $\ln\mathcal{L}(\mu,\sigma)
    \, = \, -\frac{n}{2}\ln(2\pi) - \frac{n}{2}\ln\sigma^2 - \frac{1}{2\sigma^2}\sum_{i=1}^n (x_i-\mu)^2$ for assuming that the errors scatter with a normal distribution around the model (with $RSS=SSR=\sum_{i=1}^n (x_i-\mu)^2$).
If $AIC =\ln\mathcal{L} +2k$ why does How can I apply Akaike Information Criterion and calculate it for Linear Regression? and e.g. statsmodels https://github.com/statsmodels/statsmodels/blob/2eac8066b068a88f00a29f8ff728b04e58248375/statsmodels/regression/linear_model.py#L595 compute AIC using log likelihood function llf=-log(SSR)+... shouldn't it be llf=-SSR+...(without a log applied to SSR)?

This question is about the Aikake Information Criterion (AIC) of a linear regression. However, the gist is to understand how we can write the likelihood $\operatorname{L}$ as a function of the residual sum of squares $\operatorname{RSS}$. Depending on whether we assume the error variance $\sigma^2$ is known or not, the likelihood contains either $-\log(\operatorname{RSS})$ or $-\operatorname{RSS}$.
The AIC is defined as $-2\log(\operatorname{L}) + 2k$ where $k$ is the number of parameters.
What are the parameters in a linear regression? The model is $Y_i = \mathbf{x}_i\boldsymbol{\beta} + \epsilon_i$ for observations $i = 1,\ldots,n$ and the errors $\epsilon_i$ are iid $\operatorname{N}(0,\sigma^2)$.
Sometimes we know the error variance $\sigma^2$ or at least have a reliable estimate of it, $\hat{\sigma_r}^2$. The parameters of the linear regression are the regression coefficients $\beta_1,\ldots,\beta_d$ and $k = d$. We can show that in this case:
$$
\log(\operatorname{L}) \propto - \operatorname{RSS} / (2\hat{\sigma_r}^2)
$$
And since $\hat{\sigma_r}^2$ is known, we can ignore it together with the other constant terms.
Otherwise, we estimate the error variance together with the regression coeffcients and $k = d + 1$. We can show that in this case (with $\hat{\sigma}^2=RSS/n$):
$$
\log(\operatorname{L}) \propto -n \log(\operatorname{RSS})
$$
See the derivations in more detail here.
