3 papers
astro-ph.CO2026
C3NN-SBI: Learning Hierarchies of -Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
Kai Lehman, Zhengyangguang Gong, David Gebauer +2
Cosmological analyses are moving past the well understood 2-point statistics to extract more information from cosmological fields. A natural step in extending inference pipelines t…
astro-ph.CO2025
Simulation-based inference with the integrated 3PCF
David Gebauer, Anik Halder, Stella Seitz +1
We present , a simulation-based inference (SBI) framework for analysing a higher-order weak lensing statistic, the integrated 3-point correlation function (i3PCF)…
astro-ph.CO2024
C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses
Zhengyangguang Gong, Anik Halder, Annabelle Bohrdt +2
Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNN…