Project 09: Solar Activity and Astrological Quality of Time #

Source: bigastrologybook.com/2/research/19/project-9 Archive Date: 2026-03-21 Book: The Big Astrology Book of Research by Renay Oshop Status: Methodology and Open Research — analysis not completed

What This Chapter Is #

This project documents a research design that was fully specified but not executed. The analysis scripts were written; the solar data collection methodology was established; but the critical missing component — a viable crowd-sourced mood dataset — was never obtained at scale. Rather than present fabricated results, this chapter stands as a methodology paper: what would an honest scientific test of the "solar activity ↔ human experience" hypothesis look like?

The question itself is worth preserving, because it is genuinely distinct from the rest of this book's research and more physically plausible than most astrological claims.

Research Question #

Is there a measurable correlation between objective solar activity indicators — sunspot numbers, solar flux (F10.7), and geomagnetic indices (Kp, Dst, Ap) — and subjective human mood or collective sentiment, when both are measured daily over multi-year periods?

Hypothesis #

Solar activity, particularly geomagnetic disturbances, correlates with aggregate human mood at statistically detectable but practically small effect sizes, with a lag of 0–3 days.

Why This Question Matters #

Most of this book tests whether planetary positions (where Jupiter is in the zodiac, whether Moon is in Pisces) correlate with outcomes. This project asks a different question: whether the intensity of the Sun's output affects human behavior.

These are mechanistically distinct:

• Planetary position hypotheses require some unknown mechanism by which geometric angle between distant bodies influences people — no physically plausible mechanism has been identified.
• Solar activity hypotheses have a candidate physical mechanism: geomagnetic storms alter the Earth's magnetic field, which influences melatonin secretion, disrupts sleep, and has documented associations with cardiovascular events and mood disorders in epidemiological literature.

This distinction means the solar activity question sits closer to legitimate biophysics than the rest of the book's content.

Existing Scientific Literature #

The peer-reviewed literature contains relevant precedents:

The literature is inconclusive — not because the question is unanswerable, but because most studies have small N, short time windows, or poor mood measurement. A well-powered modern study using daily data over 10+ years would provide a more definitive answer.

Proposed Methodology #

Solar Data (Available) #

NOAA's Space Weather Prediction Center publishes free daily archives:

All of these are freely downloadable with complete records dating to the 1940s and 1950s.

Mood Data (The Obstacle) #

The hard part is mood. Several approaches exist, each with limitations:

The project stalled at this step. No suitable mood dataset was identified and acquired. The solar data collection scripts were written and tested; the analysis pipeline (time-lagged regression, Granger causality, PCA on mood variables) was fully designed.

Proposed Analysis Pipeline #

Had the mood data been obtained, the analysis would have proceeded as follows:

Step 1: Stationarity #

Both solar activity series and mood series have known periodicities (the 11-year solar cycle; weekly mood patterns). These would be removed via seasonal decomposition before correlation analysis to avoid spurious correlations.

Step 2: Cross-Correlation at Multiple Lags #

Pearson and Spearman correlations between each solar measure and mood, at lags from −7 to +14 days. Expected biological mechanism (melatonin disruption, sleep disturbance) suggests a 1–3 day lag.

Step 3: Granger Causality #

Tests whether past solar values improve prediction of future mood beyond what past mood alone predicts. This is a directional test that goes beyond simple correlation.

Step 4: Principal Component Analysis #

If multiple mood variables are available (e.g., different sentiment dimensions), PCA reduces them to composite mood components before modeling.

Step 5: Control Variables #

The key confound is season: solar flux and human mood both have annual cycles, and both correlate with day length. Partial correlation controlling for day-of-year is essential.

What a Positive Result Would and Would Not Mean #

A positive correlation between geomagnetic activity and aggregate mood would be:

• Evidence for: A measurable physical influence of solar environment on human affect, consistent with existing epidemiological literature
• Not evidence for: Traditional astrological claims about planetary positions (Kp index has nothing to do with whether Mars is in Scorpio)
• Not sufficient to establish: Causation (both solar activity and mood could respond to a third variable, such as weather)

A negative result — no correlation after controlling for season and autocorrelation — would not be surprising. The epidemiological literature is inconsistent, and many null results have been published.

Why This Deserves Completion #

Of all the projects in this series, this one has the most legitimate scientific footing. The mechanism is plausible; the data is largely free; the analysis tools are standard; and a well-powered null result would itself be a useful contribution to the epidemiological literature. What's missing is approximately 10–20 GB of mood proxy data and the time to process it.

Viable next steps for a researcher wanting to complete this project:

1. Download F10.7 and daily Kp data from NOAA (ftp://ftp.swpc.noaa.gov/pub/indices/)
2. Use Reddit Pushshift or Google Trends as mood proxies (acknowledging limitations)
3. Align to daily time series, control for weekday effects and seasonality
4. Run cross-correlation and Granger causality with 0–7 day lags
5. Report both the direction and confidence interval of any correlation, not just the p-value

Conclusion #

This project presents a research design, not a result. The question — whether solar electromagnetic activity correlates with human mood — is scientifically legitimate and underexplored. The methodology is sound; the solar data is readily available; the challenge is obtaining a suitable mood proxy at scale.

The contrast with the rest of this book is instructive: while projects testing zodiac signs and planetary positions consistently return null results, the solar activity question at least provides a physical mechanism to test. Whether that mechanism produces detectable effects on human mood remains genuinely open.

Archived code and raw data outputs preserved in backup/.

Solar Activity and Astrological Quality of Time #

Research Question #

Is there a correlation between objective solar activity measures (sunspots, solar flux, geomagnetic indices) and subjective reports of mood, energy, or perceived "quality" of time periods?

Hypothesis #

Solar activity indicators correlate with crowd-sourced mood data, potentially providing a physical mechanism for some astrological timing claims.

Background #

While astrology typically focuses on planetary positions, the Sun's variable activity (11-year cycle, flares, geomagnetic storms) has measurable effects on Earth. This research tests whether solar activity correlates with subjective human experiences that astrology traditionally attributes to other factors.

Data Sources #

• Solar flux data: NOAA Space Weather Prediction Center
• Geomagnetic indices: Kp, Dst, Ap indices from NOAA/WDC
• Crowd-sourced mood tracking: Daylio, mood tracking apps (aggregated)
• Social media sentiment: Twitter/Reddit mood indicators

Mathematical Methods #

1. Time-lagged regression: Testing delayed effects of solar activity
2. Principal component analysis: Reducing dimensionality of mood data
3. Causality tests: Granger causality between solar and mood measures
4. Cross-correlation: Identifying optimal lag relationships

Implementation Plan #

Step 1: Data Collection #

• Download solar flux and sunspot data from NOAA
• Obtain geomagnetic index data (Kp, Dst)
• Collect mood/sentiment data from available sources
• Align all time series to common dates

Step 2: Data Preprocessing #

• Normalize solar activity measures
• Aggregate mood data to daily means
• Create lagged versions of solar variables
• Handle missing data appropriately

Step 3: Exploratory Analysis #

• Plot time series together
• Calculate basic correlations at various lags
• Identify potential patterns visually

Step 4: Statistical Modeling #

• Fit time-lagged regression models
• Perform Granger causality tests
• Apply PCA to mood variables
• Test multiple solar activity measures

Step 5: Validation #

• Cross-validate across different time periods
• Test robustness to different mood measures
• Compare effect sizes to known mood factors

Expected Outputs #

• Correlation analysis between solar and mood data
• Optimal lag estimates for any relationships
• Visualization of patterns
• Assessment of effect sizes

Required Python Libraries #

pandas
numpy
scipy
statsmodels
scikit-learn
matplotlib
seaborn
requests (for API access)

Ethical Considerations #

• Use only aggregated mood data
• Distinguish correlation from causation
• Discuss alternative explanations

Data Provenance #

Solar Activity #

• Source: WDC-SILSO (Royal Observatory of Belgium).
• Dataset: Daily Total Sunspot Number.
• Link: https://www.sidc.be/silso/datafiles