Data Analysis Pipeline: Python Code Concepts for Signal Detection and Verification
DATA ANALYSIS PIPELINE
PYTHON ANALYSIS (NumPy + SciPy)
Step A: Load raw IQ data from RTL-SDR recording
iq = np.fromfile("sirius_recording.raw",
dtype=np.complex64)
Step B: High-resolution FFT
# Use very long segments for Hz-level resolution
f, Pxx = welch(iq, fs=2.4e6, nperseg=2**24,
noverlap=2**23)
# This gives ~0.14Hz resolution
Step C: Extract ±100Hz around hydrogen line
window = slice(center_idx - 700, center_idx + 700)
f_zoom = f[window]
P_zoom = Pxx[window]
Step D: Search for Schumann sidebands
for sr_freq in schumann:
# Check for peaks at ± sr_freq from center
idx_pos = np.argmin(abs(f_zoom - (center + sr_freq)))
idx_neg = np.argmin(abs(f_zoom - (center - sr_freq)))
# Compare peak power to local noise floor
snr_pos = P_zoom[idx_pos] / np.median(P_zoom)
snr_neg = P_zoom[idx_neg] / np.median(P_zoom)
Step E: Statistical significance
If SNR > 3 at MULTIPLE sidebands: very interesting.
If SNR > 5 at evenly-spaced intervals: detection.
VERIFICATION CHECKLIST
□ Signal present ON-source (Sirius) only? □ Signal absent when pointed at blank sky? □ Sidebands evenly spaced at ~7.83Hz? □ Sidebands narrower than 1Hz bandwidth? □ Signal shows Doppler drift consistent with
□ Signal repeats on multiple observation nights? □ Signal NOT correlated with local RFI? □ Signal NOT correlated with satellite passes?
IF ALL BOXES CHECKED:
This would be the most important detection
in human history.
IF NO SIGNAL FOUND:
Our RTL-SDR has limited sensitivity.
Signal may be below our noise floor.
Professional telescopes (ALMA, VLA, SKA)
would need to follow up with 10,000× more
sensitivity. But we will have demonstrated
the METHOD and the FREQUENCY PREDICTION.
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