Reproduce “Characterizing 4-string contact interaction using machine learning” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Harold Erbin, Atakan Hilmi Fırat
The authors introduce a neural-network method for learning Strebel geometry needed to calculate closed-string interactions on a four-punctured sphere without specifying its critical graph. It trains an accessory-parameter network with a graph-independent loss, plus a classifier for the contact-interaction region. The best four-tachyon coefficient is 72.396, versus Moeller’s 72.390 ± 0.003; this validates only the four-puncture calculation.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.