A Weighted Wavefunction Model for Experimental Collapse Data

We quantitatively model and fit experimental collapse data from ultracold force sensor experiments using a filtered, weighted quantum wavefunction approach. By representing the measured signal as a normalized, weighted Gaussian probability density, we extract collapse parameters (width σ and filter strength λ) through nonlinear least squares fitting. Our results demonstrate that a weighted wavefunction model (k(x)·ψ(x)) provides exceptional agreement with experimental data (R² ≈ 0.98), offering both physical insight and a rigorous bridge between theory and experiment.The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this workl