Critical Spectral Invariants in Random Walks with Geometric Resetting

Abstract

Stochastic resetting -- the intermittent restart of random processes -- has profoundly reshaped first-passage theory, providing a mechanism to control and optimize completion times. While the influence of resetting on mean first-passage times is now well understood, its impact on absorption probabilities in confined domains remains comparatively unexplored. We present a complete analysis of the classical gambler's ruin problem under geometric resetting. At each time step, the walker is reset to its initial position with probability gamma, or otherwise performs a biased nearest-neighbor step. Our approach proceeds in three stages. First, we derive a renewal equation for the ruin probability q_z(gamma) by conditioning on the first step. Second, we develop a spectral representation on a weighted Hilbert space that diagonalizes the transition operator and yields explicit closed-form expressions. Third, this representation enables a precise critical-point analysis in state space. Our central result is a striking geometric invariance: when the domain size a is even, the ruin