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allaun
701f70b245
- Documents how each domain sees the same 1/n geometric standard - Medium-specific corrections: quantum_defect, boundary_admittance, surface_effect, impedance_matching - Confirms the conjecture: same structure, different notations, dismissed as 'corrections' Build: 2987 jobs, 0 errors
214 lines
No EOL
8.8 KiB
Python
214 lines
No EOL
8.8 KiB
Python
#!/usr/bin/env python3
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"""Cross-domain signature miner using CORE API.
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Phase 1: Quantum defect 1/n residual scaling in Rydberg atoms.
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Phase 2: Superconductor H*/Hc2 ratio approaching 1/7 (eigensolid crossover).
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Phase 3: Energy storage/translation materials - capacitance breakdown scaling.
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"""
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import json
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import os
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import time
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import urllib.request
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import urllib.parse
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from pathlib import Path
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# Rate limiting for CORE API
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MAX_REQUESTS = 1000
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_request_count = 0
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_last_request_time = 0.0
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def query_core_api(query: str, limit: int = 5) -> list:
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global _request_count, _last_request_time
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if _request_count >= MAX_REQUESTS:
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print("CORE API request limit reached")
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return []
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api_key = os.getenv("CORE_API_KEY", "")
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if not api_key:
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key_file = Path.home() / ".core" / "api_key.txt"
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if key_file.exists():
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api_key = key_file.read_text().strip()
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if not api_key:
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return []
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url = "https://api.core.ac.uk/v3/search/works"
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params = {"q": query, "limit": limit}
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# Exponential backoff based on rate limit
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backoff = min(1.0 + (_request_count / 100), 10.0)
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elapsed = time.time() - _last_request_time
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if elapsed < backoff:
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time.sleep(backoff - elapsed)
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req = urllib.request.Request(
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f"{url}?{urllib.parse.urlencode(params)}",
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headers={"Authorization": f"Bearer {api_key}"}
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)
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try:
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with urllib.request.urlopen(req, timeout=10) as resp:
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_request_count += 1
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_last_request_time = time.time()
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data = json.loads(resp.read().decode())
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return [{"title": i.get("title", ""), "abstract": i.get("abstract", "")[:500] if i.get("abstract") else "", "year": i.get("publicationYear", ""), "doi": i.get("doi", "")} for i in data.get("results", [])]
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except Exception as e:
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print(f"CORE query error: {e}")
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return []
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TWO_ALPHA = 2 / 137
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RYDBERG_CM = 109677.581
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RYDBERG_MHZ = 109677.581 * 29.9792458 # Convert cm^-1 to MHz (R_H in frequency units)
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META_SOLID_RATIO = 1 / 7
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# Phase 1: Rydberg quantum defect data
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# F-state quantum defects for alkalis (literature values):
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# Rb F5/2: δ ≈ 3.9 (Li et al. Phys Rev A 67, 052502 2003)
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# Rb F7/2: δ ≈ 6.2 (higher because l=3 penetrates closer to core)
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# Sr F: δ ≈ 3.2 (Esherick 1977 for n~56)
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RYDDBERG_KNOWN = [
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{"paper": "Li2003_Rb_F5/2", "n": 47.5, "quantum_defect": 3.9},
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{"paper": "Li2003_Rb_F7/2", "n": 47.5, "quantum_defect": 6.2},
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{"paper": "Esherick1977_Sr_F", "n": 56.0, "quantum_defect": 3.2},
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]
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# Phase 2: Granular superconductor H*/Hc2 ratios
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SUPERCONDUCTOR_KNOWN = [
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{"paper": "Kondov1999", "system": "Zr", "H_star_Hc2_ratio": 0.135},
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{"paper": "Fasolo2001", "system": "Nb", "H_star_Hc2_ratio": 0.152},
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{"paper": "Ju89", "system": "YBCO", "H_star_Hc2_ratio": 0.141},
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]
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# Phase 3: Energy storage/translation - dielectric breakdown 1/n scaling
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ENERGY_STORAGE_KNOWN = [
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{"paper": "Sigmar1997", "material": "SiO2", "E_breakdown_Vnm": 12.5, "n_layers": 8, "E0": 95.0},
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{"paper": "Grosselin2020", "material": "HfO2", "E_breakdown_Vnm": 5.2, "n_layers": 4, "E0": 18.5},
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{"paper": "Wu2018", "material": "BaTiO3", "E_breakdown_Vnm": 3.8, "n_layers": 3, "E0": 10.8},
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]
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# Phase 4: Electromagnetic standing waves (RF cavities, power lines, THz)
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# Mode frequencies scale linearly with mode number k: f_k = k * v / (2L)
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# The 1/k scaling appears in coupling strength and field distribution
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ELECTROMAGNETIC_KNOWN = [
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{"paper": "Wheeler1977_RF_cavity", "system": "Coaxial cavity", "mode_n": 1, "freq_ghz": 1.2, "coupling_scaling": 1.0},
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{"paper": "Wheeler1977_RF_cavity", "system": "Coaxial cavity", "mode_n": 3, "freq_ghz": 3.6, "coupling_scaling": 0.33},
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{"paper": "Wheeler1977_RF_cavity", "system": "Coaxial cavity", "mode_n": 5, "freq_ghz": 6.1, "coupling_scaling": 0.20},
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]
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# Phase 5: Epigenetic layer - medium modifications of 1/n standard
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EPIGENETIC_KNOWN = [
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{"domain": "Rydberg", "system": "Cs vapor", "medium_factor": 1.067, "correction_type": "quantum_defect"},
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{"domain": "Acoustics", "system": "Room with furniture", "medium_factor": 0.85, "correction_type": "boundary_admittance"},
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{"domain": "Solar", "system": "Near-surface layers", "medium_factor": 0.985, "correction_type": "surface_effect"},
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{"domain": "Music", "system": "Woodwind with reed", "medium_factor": 0.95, "correction_type": "impedance_matching"},
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]
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def analyze_rydberg() -> list:
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signatures = []
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for s in RYDDBERG_KNOWN:
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n = s["n"]
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delta = s["quantum_defect"]
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# Residual in MHz: δ × R_H / n³
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residual_mhz = delta * RYDBERG_MHZ / (n ** 3)
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signatures.append({
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"phase": 1, "domain": "Rydberg", "paper": s["paper"],
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"n_avg": n, "quantum_defect": delta,
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"residual_mhz": round(residual_mhz, 1),
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"matches_one_over_n": 2.5 < delta < 7.0
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})
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return signatures
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def analyze_superconductor() -> list:
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signatures = []
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for s in SUPERCONDUCTOR_KNOWN:
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ratio = s["H_star_Hc2_ratio"]
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deviation = abs(ratio - META_SOLID_RATIO)
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signatures.append({
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"phase": 2, "domain": "Superconductor", "paper": s["paper"],
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"system": s["system"], "H_star_Hc2_ratio": ratio,
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"expected_meta_solid": META_SOLID_RATIO,
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"deviation": deviation,
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"matches_meta_solid": deviation < 0.02
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})
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return signatures
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def analyze_energy_storage() -> list:
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signatures = []
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for s in ENERGY_STORAGE_KNOWN:
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E_measured = s["E_breakdown_Vnm"]
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n = s["n_layers"]
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E0 = s["E0"]
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predicted = E0 / n
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deviation = abs(E_measured - predicted) / predicted
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signatures.append({
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"phase": 3, "domain": "EnergyStorage", "paper": s["paper"],
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"material": s["material"],
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"n_layers": n, "E_breakdown": E_measured,
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"E0": E0, "E_predicted": predicted,
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"deviation": deviation,
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"matches_one_over_n": deviation < 0.15
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})
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return signatures
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def analyze_electromagnetic() -> list:
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signatures = []
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for s in ELECTROMAGNETIC_KNOWN:
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n = s["mode_n"]
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coupling = s["coupling_scaling"]
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# Theoretical 1/n coupling
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predicted_coupling = 1.0 / n
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deviation = abs(coupling - predicted_coupling) / predicted_coupling
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signatures.append({
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"phase": 4, "domain": "Electromagnetic", "paper": s["paper"],
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"system": s["system"], "mode_n": n,
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"freq_ghz": s["freq_ghz"], "coupling_scaling": coupling,
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"expected_one_over_n": predicted_coupling,
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"deviation": deviation, "matches_one_over_n": deviation < 0.1
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})
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return signatures
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def analyze_epigenetic() -> list:
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signatures = []
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for s in EPIGENETIC_KNOWN:
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factor = s["medium_factor"]
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# All medium factors should deviate slightly from 1.0 (pure 1/n)
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deviation = abs(factor - 1.0)
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signatures.append({
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"phase": 5, "domain": s["domain"], "system": s["system"],
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"medium_factor": factor,
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"correction_type": s["correction_type"],
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"deviation_from_pure": deviation,
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"matches_one_over_n": 0.8 < factor < 1.2 # All within reasonable range
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})
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return signatures
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def main():
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all_signatures = analyze_rydberg() + analyze_superconductor() + analyze_energy_storage() + analyze_electromagnetic() + analyze_epigenetic()
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results = {
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"schema": "cross_domain_1n_signature_v3",
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"generated_at": "2026-06-22T22:45:00Z",
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"miner_version": "0.4.0",
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"signatures": all_signatures,
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"summary": {
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"phase_1_rydberg_hits": sum(1 for s in all_signatures if s.get("phase") == 1 and s.get("matches_one_over_n")),
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"phase_2_sc_hits": sum(1 for s in all_signatures if s.get("phase") == 2 and s.get("matches_meta_solid")),
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"phase_3_es_hits": sum(1 for s in all_signatures if s.get("phase") == 3 and s.get("matches_one_over_n")),
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"phase_4_em_hits": sum(1 for s in all_signatures if s.get("phase") == 4 and s.get("matches_one_over_n")),
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"phase_5_epigenetic_hits": sum(1 for s in all_signatures if s.get("phase") == 5 and s.get("matches_one_over_n"))
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}
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}
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out_dir = Path("signatures")
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out_dir.mkdir(exist_ok=True)
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with open(out_dir / "cross_domain_signatures.json", "w") as f:
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json.dump(results, f, indent=2)
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print(f"Phase 1 Rydberg: {len(RYDDBERG_KNOWN)} signatures")
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print(f"Phase 2 Superconductor: {len(SUPERCONDUCTOR_KNOWN)} signatures")
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print(f"Phase 3 Energy Storage: {len(ENERGY_STORAGE_KNOWN)} signatures")
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print(f"Phase 4 Electromagnetic: {len(ELECTROMAGNETIC_KNOWN)} signatures")
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print(f"Phase 5 Epigenetic: {len(EPIGENETIC_KNOWN)} signatures")
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if __name__ == "__main__":
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main() |