e5a812fa14
Scripts 01-08 implement the complete cosmic-ray/earthquake correlation analysis from data ingestion through out-of-sample validation and combined timeseries sinusoid fitting. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
35 lines
1.7 KiB
Markdown
35 lines
1.7 KiB
Markdown
# Cosmic Ray / Earthquake Correlation Study
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## Scientific context
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Testing Homola et al. 2023 (JASTP 247, 106068, DOI 10.1016/j.jastp.2023.106068)
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which claims a 6σ correlation between cosmic ray rate variations and global seismic
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activity at a 15-day lag, with ~11-year periodicity. Prior replication attempt
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using independent NMDB data failed. This project is a rigorous methodological
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replication + stress test.
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## Key hypotheses to test
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1. The 15-day lag signal survives phase-randomised surrogate null models
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2. The signal survives solar cycle detrending (both series share an ~11-year component)
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3. The signal is stable across NMDB station subsets
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4. The signal is stable across time windows (e.g., 1980-95, 1995-2010, 2005-19)
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5. The signal appears with seismic moment (M₀ ∝ 10^(1.5Mw)) not raw magnitude sum
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## Data sources
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- NMDB: http://nest.nmdb.eu (44 stations, pressure-corrected hourly neutron counts)
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- USGS: earthquake catalogue (M≥4.5 for completeness)
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- SIDC/KSO: daily sunspot numbers
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- Reference: Pierre Auger scaler data (optional, secondary)
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## Hardware
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- CPU: Xeon E5-2680 v4 (14 physical / 28 logical cores)
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- RAM: 60 GB
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- GPU: Nvidia M40, 12 GB VRAM, compute capability 5.2 (CUDA 11.x max)
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- Prefer CuPy over PyTorch for FFT work; M40 does not support bfloat16
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## Code standards
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- Python 3.11+, type hints required for public functions
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- pytest for all data-transformation functions
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- Keep raw data in `data/raw/`, processed in `data/processed/`, results in `results/`
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- All station metadata (lat, lon, altitude, rigidity cutoff) in `config/stations.yaml`
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- Seed all random operations; log seed + git SHA in every results file
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- Use `polars` or `pandas` with `pyarrow` backend (not default pandas) for speed
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