Graduate Identification of a probability density function

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The probability density function presented is complex and involves parameters a0, a1, c0, and c1. The discussion suggests that by substituting u = x^2/2, the transformed variable u follows a gamma distribution with specific parameters. The identification of this distribution is crucial for understanding its properties and applications. The original function is defined for x in the interval (0, ∞). This analysis indicates a connection to gamma distributions, which may aid in further exploration of its characteristics.
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Identification of a probability density function.
I have the following probability density function:

$$f(x) = \frac{2^{\frac{1}{2}~\frac{a_{0}-c_{0}}{a_{0}}}~a_{1}^{(-\frac{1}{2}~\frac{a_{0}+c_{0}}{a_{0}})}~c_{1}^{(\frac{1}{2}~\frac{a_{0}+c_{0}}{a_{0}})}~x^{\frac{c_{0}}{a_{0}}}~e^{-\frac{1}{2}~\frac{c_{1}~x^{2}}{a_{1}}}}{\Gamma(\frac{1}{2}~\frac{a_{0}+c_{0}}{a_{0}})}$$

Is this a known probability density function?
 
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Support: ##x \in (0, \infty)##.
 
I don't know what you call that distribution, but if you make the substitution u = x2/2 then u has a gamma distribution with α = (c0/a0+1)/2 and β = a1/c1.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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