Statistical physics reflection principle

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The discussion revolves around calculating the probability of a random walker reaching a point m>0 in N steps, starting from k>0, with an absorbing wall at 0. The reflection principle is identified as a key concept for solving the problem, particularly in relation to boundary value problems. The linear nature of the diffusion equation ensures a unique solution based on consistent boundary conditions, which in this case requires that the probability of reaching 0 in 0 steps is zero. The solution involves a linear combination of Gaussians that satisfy the boundary condition at the absorbing wall. Understanding these principles is essential for deriving the appropriate diffusion equation in this context.
Dr.Lecter
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Homework Statement



Consider a random walker in one dimension, which can take right or left steps with equal probability. Assume that the walker starts at the location k>0 and there is an absorbing wall at point labelled as 0, that means if the walker reaches 0 the process stops and he stays there. Calculate the probability of reaching point m>0 in N steps. By taking continuum limit, find the solution to the diffusion in the presence of an absorbing wall at the origin.

The Attempt at a Solution



Actually this is an example of reflection principle (method of imagines boundary value problems.) However I couldn't figure out how its related. So if you give me hint to where should I start I'll be appreciated.
 
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I'm assuming you know what to do without the absorbing wall (i.e. how to derive the appropriate diffusion equation and show that the solution is Gaussian of some particular width in the continuum limit etc. etc. etc.) The diffusion equation is linear and has a unique solution given complete and consistent boundary conditions. So, given some set of boundary conditions, the solution will always be some linear combination of same-width Gaussians. For an absorbing wall at 0, the boundary condition is that the probably of reaching 0 in 0 steps is 0. Up to a sign (and only one sign will make sense here) there is one unique linear combination of same-width Gaussians that gives the value 0 at the point 0.
 
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