I’m given a signal $x(t)$, it's convolved with $h(t)$ and sampled at rate T=1. The result is called $\tilde{x}[n]$.
For $$h(t) = \begin{cases} 1 & -0.5<t\le 0.5 \\ 0 & \text{else} \end{cases},$$ find $\tilde{X}^f(\theta)$ as an expression of $X^f(\theta)$.
My attempt
I noticed that I can write for a sampled signal $$\tilde{X}^f(\theta) = \frac{1}{T} \sum_{k=-\infty}^{\infty} \tilde{X}^F\left(\omega = \frac{(\theta - 2\pi k)}{T}\right).$$
All that I had left to do was simplify using the convolution to multiplication relation and this identity:
$$\begin{cases} k & |t|<t_0 \\ 0 & \text{else} \end{cases} \to 2kt_0 \text{sinc}(\omega t_0),$$ I substituted $\tilde{X}^F(\omega)$ into the expression.
Results
$$\sum_{k=-\infty}^{\infty} X^F(\theta - 2\pi k)\operatorname*{sinc}(0.5(\theta-2\pi k))$$
whereas what they got: $$\sum_{k=-\infty}^{\infty} X^F(\theta - 2\pi k)\operatorname*{sinc}\left(\frac{\theta-2\pi k}{2\pi}\right)$$
I'm losing my mind over the $\pi$ in the denominator, why is it there?