We present the Citizen Orb Photometry Pipeline (COPP), a standardized ten-step computational method for quantitative analysis of anomalous luminous phenomena (ALP) captured in consumer video recordings. The pipeline extracts frame-by-frame photometric measurements including centroid position, apparent size, peak and integrated intensity, RGB spectral channel decomposition, and derived metrics including blue excess, pulsation periodicity, and position discontinuities. A key methodological innovation is adaptive spectral measurement: when sensor saturation renders core color data unreliable, the pipeline automatically shifts spectral analysis to the unsaturated halo region, enabling consistent cross-case comparison regardless of source brightness or observer distance. We demonstrate COPP through comparative analysis of three ALP captures spanning fourteen years, three countries, and distinct observational conditions: a distant, mobile, spectrally cool orb over the Taiwan coast (2021); a close-range, stationary, spectrally warm orb in the United Kingdom (2011); and a close-range, stationary orb with correlated size-color pulsation in Poland (2025). Despite differences in apparent brightness, angular size, and sensor saturation state, all three cases exhibit spectral overlap consistent with a common emission mechanism. Signal structure analysis of the Taiwan case reveals strong size-intensity correlation (r = 0.808) and quasi-periodic pulsation, indicating non-random temporal structure. COPP addresses a documented gap between professionally calibrated instruments and citizen capture platforms by providing a standardized computational bridge that runs in standard Python environments and processes any consumer video format. The COPP v1.0 pipeline demo is available as a working prototype at https://copp-w.vercel.app/. The purpose of this paper is to place the method and its outputs in the public domain in a form others can inspect, critique and extend, and we invite the community to test the pipeline against their own captures and to help make it more reliable and more consistent.
The current study is an extension of two previous studies using the five-letter word puzzle Wordle to test whether there was an improvement in player performance throughout the day that might be suggestive of morphic resonance. Players have six attempts to solve the puzzle. The previous studies, which used WordleBot to obtain continuously updated performance statistics for millions of players, found inconsistent results. In the first study, the percentage of players solving in one to three attempts increased throughout the day. In the second study, success rates declined across all six attempts. To increase sensitivity in the current study, we sampled over a much longer time span, starting with samples at 4:00 a.m. New Zealand Daylight Time (NZDT), near the International Date Line, where each new day’s puzzle is launched, followed by samples around midday GMT, and ending with samples near midnight Hawaii Standard Time (HST), close to the end of the international day (giving a total span of 43 hours). The results showed that there is a repeatable pattern whereby there is an increase throughout the day in the percentage of players solving the puzzle in one or two attempts, and a decrease in players solving it in four to six attempts. We discuss possible explanations for this pattern, including time-of-day effects, cheating, precognition, and morphic resonance.
