Welcome! I'm David Gebauer
Cosmology PhD Student at Universität Bielefeld (he/him)
I am a PhD student in the Faculty of Physics at Bielefeld University. My research is in weak gravitational lensing cosmology, where I work on higher-order statistics and simulation-based inference. I am particularly interested in making machine learning methods for cosmological analyses interpretable.
Research Areas
Weak Gravitational Lensing
Using the distortion of galaxy shapes by large-scale structure to constrain cosmological parameters, with a focus on higher-order shear statistics beyond the standard two-point functions.
Simulation-Based Inference
Developing likelihood-free inference pipelines that use forward-modelled simulations to constrain cosmology, bypassing the need for analytical likelihood expressions.
Interpretable Machine Learning
Building neural network architectures whose internal representations correspond to known physical quantities, such as N-point correlation functions, to keep machine learning analyses transparent and physically meaningful.
Recent Publications
C3NN-SBI: Learning Hierarchies of N-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
arXiv:2602.16768 • 2026
SBi3PCF: Simulation-based inference with the integrated 3PCF
arXiv:2510.13805 • 2025
Cosmology with second and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis
Phys. Rev. D 112, 123514 • 2025
C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses
ApJ 971 156 • 2024
DES Year 3: Cosmology with the Integrated 3-point Correlation Function of cosmic shear
in prep. •
Application of SBi3PCF to DES Y3 Catalog
in prep. •