Spectral Gap Selection of Lorentzian Signature from Observer Fisher Information Geometry

Key Points

• The research aims to explore how sparse Fisher information matrices select Lorentzian signatures through a spectral gap mechanism.
• Analyzed sparse Fisher information matrices for Ising-type models
• Utilized spectral gap analysis to identify Lorentzian signatures
• Established stability bounds for the matrices
• Demonstrated W(q=1) > W(q>=2) for the matrices
• Reinforced the preference for Lorentzian signatures in sparse information matrices
• Provided explicit stability bounds confirming the findings