We report preliminary consistency checks of the Stochastic Rupture (SR) collapse framework usingthree classes of publicly available data. These are exploratory analyses intended to identify whetherSR predictions are consistent with existing observations -- not to claim confirmation of theframework. (i) In Google Willow surface code data (450, 000 shots, N=104 detectors), we find HurstH=0. 781 and chiₑff skewness=6. 02, consistent with SR prediction of long-range relational memoryin quantum hardware noise. (ii) In the S8 tension dataset (7 surveys, 2020-2024), an SR-motivatedsuppression model S8 (z) = S8LCDM * sqrt (1 - e0* (1+z) ^-2) fits the data with chi2/ndof=0. 46 vs8. 10 for LCDM, with a free parameter e0=0. 249 +/- 0. 035. AIC/BIC model selection places SRamong the preferred single-parameter models. (iii) A BAO geometric test yields e0BAO=0. 000 +/-0. 003, consistent with SR prediction that background geometry is unaffected -- in 7. 1 sigma tensionwith e0S8, a pattern that SR predicts and that tentatively discriminates it from massive neutrinos. In LIGO/Virgo data the test is inconclusive due to methodological limitations. All results are reportedwith explicit caveats and open problems.
GUILHERME ZAMBUZI (Fri,) studied this question.