6 papers
Exploring Line Bundle Standard Models with Transformers
Jacky H. T. Yip, Alessandro Mininno, Gary Shiu
We propose a Transformer-based Reinforcement Learning architecture, "LB-Explorer", to search for heterotic line bundle standard models arising from compactifications on smooth Cala…
Generating Special Triangulations with Transformers
Charles Arnal, Jacky H. T. Yip, François Charton +1
Triangulations, i.e., well-structured decompositions of geometric objects into triangle-like pieces, are central objects in many domains of mathematics and physics. In particular,…
Primordial non-Gaussianity -- Fast simulations and persistent summary statistics
Juan Calles, Gabriella Contardo, Jorge Noreña +3
We investigate the sensitivity of topological and traditional summary statistics to primordial non-Gaussianity (PNG) using two suites of simulations. First, we introduce a new simu…
Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers
Jacky H. T. Yip, Charles Arnal, François Charton +1
Fine, regular, and star triangulations (FRSTs) of four-dimensional reflexive polytopes give rise to toric varieties, within which generic anticanonical hypersurfaces yield smooth C…
Cosmology with Persistent Homology: Parameter Inference via Machine Learning
Juan Calles, Jacky H. T. Yip, Gabriella Contardo +3
Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pi…
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
Jacky H. T. Yip, Adam Rouhiainen, Gary Shiu
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topolo…