IONOSPHERE

Temporal structure in consciousness-RNG interactions, and applied coherence sampling.

The question

Ionosphere asks a narrow, falsifiable question: when an audience is engaged with an immersive audiovisual installation driven by a quantum random number generator, does the QRNG output exhibit detectable temporal structure -- bursts, clustering, autocorrelation, periodicity -- that is absent from a parallel RNG nobody is watching?

Most prior consciousness-RNG work has looked for sustained directional drift in the mean of the bitstream [Jahn et al. 1997][Radin & Nelson 1989]. Ionosphere takes a different starting hypothesis: if there is a signal, it manifests as transient burst events during engagement and cancels in aggregate. The mean is not where to look. Temporal structure is.

That single methodological move -- shifting from mean drift to burst structure -- changes which tests can detect a signal. A two-tailed z-test on the bitsum will return null. A runs test, an autocorrelation, a Markov transition probability, or a Fisher's g periodicity test can light up.

The apparatus

The Ion192 chandelier is the visible end of the system: 192 glass rods with fiber-optic cores arranged in an 8x24 grid, individually addressed by a Raspberry Pi Pico over a single addressable LED chain. The light is the audience's interface to the data.

Behind the chandelier is a pattern engine that translates QRNG output into visible structure. A TrueRNG hardware source [Herrero-Collantes & Garcia-Escartin 2017] produces bits at line rate; every 60 seconds the engine takes a buffer, runs a two-tailed z-test on the bitsum, and uses the resulting p-value as a "glitch factor" between 0 and 1. When the bits look ordinary, the active pattern renders cleanly. When the bits drift away from 50/50, the glitch factor spikes and the pattern distorts -- flicker, interference, stuttered frames. A second statistic from the same buffer (chi-square uniformity across 8 byte-value bins) selects which pattern function runs at all.

The pattern engine is instrumentation, not decoration. It exists to operationalize immersion: if the proposed effect requires audience engagement, then the audience must be engaged with the QRNG itself, not with a screen showing arbitrary visuals. Coupling the visual output to the live statistical state of the bitstream means viewers are watching the data. The patterns are the experimental condition.

A live, browser-rendered version of the pattern engine is available at patterns.html. It uses pseudorandom numbers in the browser rather than a hardware QRNG, but the visual logic is identical to what runs in the field.

The experiments

Autumn Lights 2025 -- gallery exhibition

October 15-18, 2025. Three days. 3,249 one-minute observation periods, ~24.5 MB of raw RNG bytes. Audience: passive, rotating through gallery exhibits. On Friday, two devices ran in parallel -- the main installation in the attended room, plus a hidden 8x8 LED panel running the same code in a side room with no one watching it.

Aggregate result: anomaly rate 4.28%, near the expected 5%, runs test p = 0.969. No clustering at the dataset level. The Friday two-device contrast (327 attended periods vs. 273 hidden periods, unequal N) returned a statistically significant difference on two of three tests:

• Friday main lights (attended): chi-square anomaly rate 7.34%, p = 0.041
• Friday 8x8 panel (hidden): chi-square anomaly rate 4.03%, p = 0.807
• Mann-Whitney U comparison of period-level p-values: p = 0.013
• Fisher's exact test odds ratio: 1.89x attended vs. hidden, p = 0.114 (n.s.)
• Saturday (replicated attended condition): chi-square 8.42%, p = 0.008

DFA alpha across the full Autumn Lights dataset was 0.534 [Peng et al. 1994], indicating mild long-range persistence in the z-score sequence. Anomalies were elevated in the 20:00 hour, which corresponded to documented peak attendance from gallery sign-in logs. This time window was not pre-specified.

NYE 2025/2026 -- social gathering

December 31, 2025 through January 1, 2026. 48 hours. 4,149 experimental periods, 53 MB of bytes. A second TrueRNG ran in a parallel hidden control configuration -- empty room, no audience, no awareness, same building, 4-hour overlap window with the experimental device. Attendees were not told the control existed. (Control timestamps were logged in PST mislabeled as UTC and required an 8-hour correction prior to alignment with the experimental stream.)

This dataset shows the strongest temporal-burst signature observed to date. Six of 12 tests in the battery returned significant results.

The control's anomaly rate is in the same range as the experimental group's. What it lacks is the structure: zero burst clusters during the 4-hour overlap, against twelve in the experimental device. All five control anomalies occurred in isolation.

A documented set of meditation sessions during the NYE window aligned with the burst structure: Mason at 21:29, Miguel at 21:58, Justin at 22:25, Dani from 22:41 through 23:20. The first observed cluster onset was 21:52. Multiple clusters fell between 22:00 and 23:00. The largest single burst -- four consecutive anomalous periods -- occurred at 23:51, in the Q2 midnight window.

The control's DFA alpha of 0.677 is itself elevated above the 0.5 random-walk baseline, which means some long-range persistence may be intrinsic to the TrueRNG hardware rather than session-specific. This is flagged below as a known limitation requiring extended unattended-baseline runs to resolve.

The control matters

The simultaneous hidden-control design is the methodological feature that distinguishes Ionosphere from prior RNG-during-event work. Critiques of GCP-style prospective designs often turn on the absence of a synchronous null channel [Bancel & Nelson 2008][Nelson & Bancel 2011]: the experimenter analyzes a network of RNGs against pre-stated event windows, but no comparable RNG was running in identical conditions with no event attached. Ionosphere's NYE 2025/2026 design closes that loop. Two devices, same hardware, same building, same time window, identical software, one observed and one not. The structural metrics dissociate cleanly.

A third session -- a housewarming party in November 2025, 357 one-minute periods, no dedicated control device -- returned null on all 12 tests and is included in the combined meta-analysis below as a third site. Combined fixed-effects z across the three datasets is +0.0121, p = 0.295. That is the predicted result if burst structure is real and direction-balanced -- positive z-score bursts during peaks of engagement, negative bursts during refractory periods, near-zero in aggregate. A non-null mean would be inconsistent with the underlying model, not supportive of it.

The 12-test battery

No single test captures temporal burst structure. Ionosphere uses a fixed battery applied to every dataset:

1. Autocorrelation (lag-1 serial dependence)
2. Change-point detection (PELT for distribution shifts)
3. Spectral analysis / Fisher's g (periodicity) [Fisher 1929]
4. Stouffer's Z (aggregate directional)
5. Bootstrap mean Z (CI for mean z-score)
6. Entropy test (p-value uniformity)
7. Extreme value analysis (tail probability vs. Gaussian)
8. Detrended fluctuation analysis [Peng et al. 1994][Peng et al. 1995]
9. Variance ratio (trending across lags)
10. Markov transition probabilities [Anderson & Goodman 1957]
11. Time-of-day analysis (Rayleigh test for circadian patterns)
12. Lag analysis at extended lags

The pre-registered replication design proposes Markov transition probability as the primary outcome, with the remaining tests as secondary. The pipeline computes all twelve per dataset and is ready to scale.

Applied coherence sampling

A separate proof-of-concept used Ionosphere's QRNG stream as a seed source for the Weather Research and Forecasting (WRF) numerical weather model. During the January 2026 North American winter storm, an Ionosphere coherence-sampled seed produced lower quantitative precipitation forecast error than all 20 control seeds and the unperturbed control run, verified against airport observations (p < 0.05). This is preliminary and one storm only -- a single result, not a body of evidence -- but it suggests that whatever structure the burst-detection metrics are picking up may have downstream usefulness for systems that consume random seeds.

What this is not

Ionosphere does not claim to demonstrate that consciousness directly causes RNG deviations. It demonstrates that, in two field experiments, the QRNG output of an attended system carried temporal-burst structure that a parallel unattended system did not, and that the structure aligned with documented engagement events.

Mechanisms are not addressed. Whether the effect is best explained by direct mind-matter interaction (the strong reading) [Radin et al. 2012][Mossbridge et al. 2012], by environmental or experimenter-side artifacts that correlate with engagement (a confounding reading), or by selection effects in how engagement windows are chosen -- those questions require multi-site, pre-registered replications with independent analysis pipelines. We are not there yet.

Where Ionosphere connects to broader consciousness theory -- IIT [Albantakis et al. 2023], Global Workspace [Mashour et al. 2020] -- is left open. The data constrain mechanism, they do not yet identify one.

Limitations

• Neither the Autumn Lights nor NYE analysis pipelines were prospectively pre-registered. Both are exploratory. The proposed multi-site replication will pre-register Markov transition probability as the primary outcome.
• Single site, single experimenter team. No independent replication.
• DFA alpha is elevated in the control as well as the experimental data. Long-range persistence may be partially intrinsic to TrueRNG hardware. Extended unattended-baseline runs are needed.
• N for the NYE control is 132 periods over a 4-hour overlap, smaller than the 4,149-period experimental dataset. The contrast is structural rather than statistical-power-equivalent.
• Friday Autumn Lights attended (327 periods) and hidden-control (273 periods) samples were unequal in size. Power is asymmetric across the two devices.
• Meditation alignment was documented but not pre-registered. Time-of-event windows for the NYE write-up were defined post hoc against participant logs.
• The pattern engine is an unblinded variable. Future designs will run yoked sham-pattern-engine controls to isolate the audiovisual feedback variable from QRNG state.
• Effect sizes are modest. The strongest single-metric result is the 7.8x elevation in Markov transition probability during NYE; a strong effect for this literature, a small effect by general-experimental-psychology standards.

Team

• Mason U. Borchard -- principal investigator, software development, design, analysis, statistical methods, pattern engine
• Timothy D. Beach -- audio engineer, software development, hardware
• Danielle S. Caputi -- co-investigator, software development, hardware, design
• Justin Iredale -- hardware, exhibition coordination

References

1. Albantakis, L., et al. (2023). Integrated information theory (IIT) 4.0. PLOS Computational Biology, 19(10), e1011465. doi:10.1371/journal.pcbi.1011465
2. Anderson, T. W., & Goodman, L. A. (1957). Statistical inference about Markov chains. Annals of Mathematical Statistics, 28(1), 89-110. doi:10.1214/aoms/1177707039
3. Bancel, P., & Nelson, R. (2008). The GCP event experiment: Design, analytical methods, results. Journal of Scientific Exploration, 22(3), 309-333.
4. Fisher, R. A. (1929). Tests of significance in harmonic analysis. Proceedings of the Royal Society of London A, 125(796), 54-59. doi:10.1098/rspa.1929.0151
5. Herrero-Collantes, M., & Garcia-Escartin, J. C. (2017). Quantum random number generators. Reviews of Modern Physics, 89(1), 015004. doi:10.1103/RevModPhys.89.015004
6. Jahn, R. G., et al. (1997). Correlations of random binary sequences with pre-stated operator intention: A review of a 12-year program. Journal of Scientific Exploration, 11(3), 345-367.
7. Mashour, G. A., Roelfsema, P., Changeux, J.-P., & Dehaene, S. (2020). Conscious processing and the global neuronal workspace hypothesis. Neuron, 105(5), 776-798. doi:10.1016/j.neuron.2020.01.026
8. Mossbridge, J., Tressoldi, P., & Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: A meta-analysis. Frontiers in Psychology, 3, 390. doi:10.3389/fpsyg.2012.00390
9. Nelson, R. D., & Bancel, P. A. (2011). Effects of mass consciousness: Changes in random data during global events. Explore, 7(6), 373-383. doi:10.1016/j.explore.2011.08.003
10. Peng, C.-K., et al. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685-1689. doi:10.1103/PhysRevE.49.1685
11. Peng, C.-K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 5(1), 82-87. doi:10.1063/1.166141
12. Radin, D. I., & Nelson, R. D. (1989). Evidence for consciousness-related anomalies in random physical systems. Foundations of Physics, 19(12), 1499-1514. doi:10.1007/BF00732509
13. Radin, D., et al. (2012). Consciousness and the double-slit interference pattern: Six experiments. Physics Essays, 25(2), 157-171. doi:10.4006/0836-1398-25.2.157

See also

• ACORN 2026 abstract -- listed under articles
• Live patterns viewer -- patterns.html