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THE EGS5 CODE SYSTEM

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Figure 4.3: UCBEND simulation at 3.5 MeV (B=1.0 kG).<br />

It becomes clear from pictures such as these that it is easier to focus the 8.5 MeV electrons through<br />

the slit than the 3.5 MeV ones. A number of <strong>EGS5</strong> “experiments” can be performed rather easily<br />

now that the basic geometry and magnetic field transport is established. For example, one can<br />

replace the air regions and/or the copper window with vacuum in order to determine what effect<br />

they have. What was discovered by doing this is that the copper window has the greatest effect<br />

on the transmission through the slit. Note that Figure 4.4, illustrates what happens when the<br />

magnetic field is turned off. Users interested in charged particle transport in an electric field should<br />

see Rawlinson, Bielajew, Munro and Galbraith [133].<br />

4.3 Using Combinatorial Geometry with <strong>EGS5</strong><br />

All of the user codes presented thus far have treated problems with simple geometries involving<br />

few regions and only basic shapes (parallel slabs and right circular cylinders). Construction of subroutine<br />

HOWFAR for such geometries is rather straight-forward, as distances to boundaries are easily<br />

determined for planar and cylindrical surfaces, and identifying new region numbers subsequent to<br />

boundary crossings is almost trivial when there are few regions with limited intersections. However,<br />

for simulations involving complex devices such as radiotherapy accelerator heads or high energy<br />

physics detectors, subroutine HOWFAR can be quite cumbersome to construct. In such applications,<br />

regions can have complex shapes for which distance to boundary computations are more involved<br />

(e.g., in concave regions there can be multiple intersection points on the same surface), and/or have<br />

surfaces which are bordered on one side by a large number of distinct volumes (e.g., at a collimators<br />

interface), making region identification more difficult.<br />

As the need to simulate complex geometry is a common problem in Monte Carlo modeling,<br />

197

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