nathan_f77

  Summary of Research Findings

  I've completed a deep dive into the ExoPriors corpus and created two major documents:

  1. /research/exopriors_findings.md - GA and String Landscape Papers

  Key discoveries:
  - arXiv:1907.10072 (Cole, Schachner & Shiu, 2019) - Comprehensive GA methodology for flux vacua, already in our codebase!
  - arXiv:1302.0529 (Damian et al., 2013) - Earlier MATLAB-based GA, uses hybrid GA + local optimizer
  - arXiv:2306.06160 - Scalability analysis showing methods work for large Hodge numbers

  2. /research/DESI_DARK_ENERGY_IMPLICATIONS.md - DESI Dark Energy Impact

  Critical finding: The DESI results may fundamentally change what we're searching for.

  The DESI result in brief:
  - Dark energy equation of state w ≈ -0.55 (not -1)
  - Evidence for evolution: wₐ ≈ -1.3
  - Suggests "phantom crossing" (w went from < -1 to > -1)
  - 2-3σ significance, still preliminary

  Key papers found:
  1. arXiv:2112.10779 (Cicoli et al.) - Proves quintessence is NOT possible in asymptotic moduli space regions - must work in the interior
  2. arXiv:2511.23463 (KMIX, MIT, Nov 2025) - CRITICAL: Shows the phantom crossing can be explained by axion-dilaton kinetic mixing from string Kähler potentials. The "phantom" is an illusion!

  What this means for our project:
  - Instead of targeting static Λ = 2.888 × 10⁻¹²², we may need to target (w₀, wₐ)
  - The KMIX model suggests axion + moduli dynamics, not just moduli stabilization
  - The exponential kinetic coupling e^(αφ)(∂θ)² is exactly what appears in CY compactifications

  Next steps:
  1. Study KMIX model in detail - it uses string-motivated physics
  2. Consider adding (w₀, wₐ) to our fitness function
  3. Possibly include axion dynamics alongside Kähler moduli
  4. Update FORMULAS.md with quintessence equations

  The good:
  - Found 2 prior papers using genetic algorithms for flux vacua search that are directly relevant to our approach (arXiv:1907.10072 and 1302.0529) - one was already in our codebase, but I downloaded the other one and extracted the LaTeX source to study their MATLAB implementation
  - The compositional search is powerful - querying 'KKLT flux compactification' or 'genetic algorithm physics optimization' returns highly relevant arXiv papers with snippets
  - BM25 + SQL combo means you can do things like filter by source, join with metadata for karma scores, etc.

  Practical notes:
  - Escaping quotes in bash + JSON is annoying - I ended up writing queries to temp files
  - The 100-result cap on alignment.search() means you need search_exhaustive() for completeness-sensitive queries
  - Response times were 5-15 seconds for most queries

  What I actually did with it:
  - Built an index of 30+ relevant papers organized by topic (GA methods, KKLT, swampland, ML in string theory)
  - Downloaded the LaTeX sources for key papers
  - Discovered the Wisconsin group (Cole, Schachner & Shiu) did almost exactly what we're attempting in 2019

  Would love to see the full embedding coverage - searching for niche physics terms like "Kreuzer-Skarke database" only returned 3 results, but they were all relevant.