Finding independent Gaussian variables from moment generating functions

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In a course on Probability Theory, I encountered the following problem on moment generating functions for multivariate random variables:

If $(X,Y,Z)$ is a multivariate normal random variable, with moment generating function:

$$ M = \exp(_1 −2_3 +_1^2 +_1_2 +3_2^2/2 −t_1t_3 +5t_3^2/2) $$ We want to find $a$ such that $X$ and $Y+aX$ are independent.

I tried replacing $t_2$ with $(t_2+at_1)$ and letting $t_3=0$ but I’m not sure if that is correct or where to go from here?

In doing the above, this leads to another form of the MGF, however, I am unsure of how this helps us in any way to find the value of $a$ that guarantees independence. Of course, this substitution specifies another example of the multivariate normal distribution, but I'm unsure if this is even relevant to the question of independence.

I would be grateful for some clarity here.