Revision as of 19:05, 5 June 2015

Experimental Summary


Given	set of n shots {\( (h_1, v_1), (h_2, v_2), ..., (h_n, v_n) \)} All of the (h,v) positions do not need to be known so a ragged hole will suffice.
Assumptions	Ideally the shot would follow a Rayleigh Distribution \(\bar{h} \sim \mathcal{N}(\bar{h},\sigma_h^2), \bar{v} \sim \mathcal{N}(\bar{v},\sigma_v^2)\) Horizontal and vertical dispersion are independent. \(\sigma_h = \sigma_v\) (realistically \(\sigma_h \approx \sigma_v\)) \(\rho = 0\) \(PDF_{r_i}(r) = \frac{r}{\sigma^2}e^{-r^2/2\sigma^2}\) Note: It is not necessary to fit \(\sigma\) to calculate the Figure of Merit. No Fliers
Data transformation	Identify two holes, \(i, j\) which are the farthest apart.
Experimental Measure	\(ES = \sqrt{(x_i - x_j)^2 - (y_i - y_j)^2)}\),

yada yada

Theoretical \(FOM\) Distribution
Parameters Needed
\(PDF(r; \sigma)\)
\(CDF(r; \sigma)\)
Mode of PDF)
Median of PDF
Mean of PDF
Variance
Variance Distribution
(h,v) for all points?	Yes
Symmetric about Mean?	No, skewed to larger values. More symmetric as number of shots increases.

@@ Line 13: / Line 13: @@
 | Assumptions
 |
-* The shot dispersion follows a Rayleigh Distribution
+* Ideally the shot would follow a Rayleigh Distribution
 ** <math>\bar{h} \sim \mathcal{N}(\bar{h},\sigma_h^2), \bar{v} \sim \mathcal{N}(\bar{v},\sigma_v^2)</math>
 ** Horizontal and vertical dispersion are independent.
@@ Line 23: / Line 23: @@
 |-
 | Data transformation
-| Preliminary Cartesian Calculations (Conceptually)
+| Identify two holes, <math>i, j</math> which are the farthest apart.
-* <math>h_{Range} = \max\{\,h_1,\ \dots \ h_n\,\} - \min\{\,h_1,\ \dots\ h_n\}</math>
-* <math>v_{Range} = \max\{\,v_1,\ \dots \ v_n\,\} - \min\{\,v_1,\ \dots\ v_n\}</math>
 |-
 | Experimental Measure
-| <math>FOM = \frac{1}{2}(h_{Range} + v_{Range})</math><br />
+| <math>ES = \sqrt{(x_i - x_j)^2 - (y_i - y_j)^2)}</math>,
 |}