Advanced Data Analysis of Spontaneous Biophoton Emission: A Multi-Method Approach
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909902030503936 |
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| author | Benfatto, M. De Paolis, L. Tonello, L. Grigolini, P. |
| author_facet | Benfatto, M. De Paolis, L. Tonello, L. Grigolini, P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced Data Analysis of Spontaneous Biophoton Emission: A Multi-Method Approach Benfatto, M. De Paolis, L. Tonello, L. Grigolini, P. Biological Physics Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability Quantitative Methods 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. |
| title | Advanced Data Analysis of Spontaneous Biophoton Emission: A Multi-Method Approach |
| topic | Biological Physics Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability Quantitative Methods |
| url | https://arxiv.org/abs/2511.11080 |