2025/11/14 by M. Benfatto, L. De Paolis, Benfatto, M. +5
Biochemistry, Genetics and Molecular Biology · Medicine · #Adaptation and Self-Organizing Systems (nlin.AO) #Biofield Effects and Biophysics #Biological Physics (physics.bio-ph) #Chemical and Physical Studies #Data Analysis #FOS: Biological sciences #FOS: Physical sciences #Fractal and DNA sequence analysis #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2511.11080
openalex publication_date 2025/11/14 · openalex created_date 2025/11/18 · openalex updated_date 2026/07/28
Ultra-weak photon emission (UPE) from living systems is widely hypothesized to reflect un-derlying self-organization and long-range coordination in biological dynamics. However, distin-guishing biologically driven correlations from trivial stochastic or instrumental effects requires a robust, multi-method framework. In this work, we establish and benchmark a comprehensive anal-ysis pipeline for photon-count time series, combining Distribution Entropy Analysis, Rényi entro-py, Detrended Fluctuation Analysis, its generalization Multifractal Detrended Fluctuation Analysis, and tail-statistics characterization. Surrogate signals constructed from Poisson processes, Fractional Gaussian Noise, and Renewal Processes with power-law waiting times are used to validate sensitivity to memory, intermittency, and multifractality. Across all methods, a coherent hierarchy of dynamical regimes is recovered, demonstrating internal methodological consistency. Application to experimental dark-count data and attenuated coherent-laser emission confirm Poisson-like behavior, establishing an essential statistical baseline for UPE studies. The combined results show that this multi-resolution approach reliably separates trivial photon-counting statistics from struc-tured long-range organization, providing a validated methodological foundation for future biological UPE measurements and their interpretation in the context of non-equilibrium statistical physics, information dynamics, and prospective markers of biological coherence.